Training object detection models using transfer learning

ABSTRACT

Apparatuses, systems, and techniques for training an object detection model using transfer learning.

TECHNICAL FIELD

At least one embodiment pertains to processing resources used to performand facilitate operations for training an object detection model usingtransfer learning. For example, at least one embodiment pertains toprocessors or computing systems used to provide and enable one or morecomputing systems to train, using transfer learning, an object detectionmodel to detect objects of a target class that are depicted in one ormore images, according to various novel techniques described herein.

BACKGROUND

Machine learning is often applied to image processing, such asidentification of objects depicted within images. Object identificationmay be used in medical imaging, science research, autonomous drivingsystems, robotic automation, security applications, law enforcementpractices, and many other settings. Machine learning involves training acomputing system—using training images and other training data—toidentify patterns in images that may facilitate object detection.Training can be supervised or unsupervised. Machine learning models canuse various computational algorithms, such as decision tree algorithms(or other rule-based algorithms), artificial neural networks, and thelike. During an inference stage, a new image is input into a trainedmachine learning model and various target objects of interest (e.g.,vehicles in an image of a roadway) can be identified using patterns andfeatures identified during training.

BRIEF DESCRIPTION OF DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 is a block diagram of an example system architecture, accordingto at least one embodiment;

FIG. 2 is a block diagram of an example training data generator and anexample training engine, according to at least one embodiment;

FIG. 3 is a block diagram of an example object detection engine,according to at least one embodiment;

FIG. 4A depicts an example trained object detection model, according toat least one embodiment;

FIG. 4B depicts an example trained object detection model that isupdated to remove a mask head, according to at least one embodiment;

FIG. 5A illustrates a flow diagram of an example method of training amachine learning model to detect objects of a target class, according toat least one embodiment;

FIG. 5B illustrates a flow diagram of an example method of using amachine learning model that is trained to detect objects of a targetclass, according to at least one embodiment;

FIG. 6 illustrates a flow diagram of an example method of training amachine learning model and updating the trained machine learning modelto remove a mask head, according to at least one embodiment;

FIG. 7A illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 7B illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 8 illustrates an example data center system, according to at leastone embodiment;

FIG. 9 illustrates a computer system, according to at least oneembodiment;

FIG. 10 illustrates a computer system, according to at least oneembodiment;

FIG. 11 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 12 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 13 is an example data flow diagram for an advanced computingpipeline, in accordance with at least one embodiment;

FIG. 14 is a system diagram for an example system for training,adapting, instantiating and deploying machine learning models in anadvanced computing pipeline, in accordance with at least one embodiment;and

FIGS. 15A and 15B illustrate a data flow diagram for a process to traina machine learning model, as well as client-server architecture toenhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment;

FIG. 16A illustrates an example of an autonomous vehicle, according toat least one embodiment;

FIG. 16B illustrates an example of camera locations and fields of viewfor the autonomous vehicle of FIG. 16A, according to at least oneembodiment;

FIG. 16C illustrates an example system architecture for the autonomousvehicle of FIG. 16A, according to at least one embodiment; and

FIG. 16D illustrates a system for communication between cloud-basedserver(s) and the autonomous vehicle of FIG. 16A, according to at leastone embodiment.

DETAILED DESCRIPTION

Accurately detecting and classifying objects included in imagesdepicting various environments is a challenging task. Advancements havebeen made regarding machine learning models that are trained to detectobjects included in given input images. However, the accuracy of objectdetection and classification provided by a machine learning model isdependent on the data that is used to train the model. In one example, asmart surveillance system can use a machine learning model to detectobjects of a target class (e.g., a “people” or “person” class) in imagescaptured by a camera (e.g., a surveillance camera, a camera for anautonomous vehicle, etc.). In addition to detecting and classifyingobjects depicted in given input images, a machine learning model can betrained to determine one or more characteristics associated with thedetected objects. In accordance with the previous example, a machinelearning model used by a smart surveillance system to detect objects ofa target class can also be trained to predict a position of the detectedobject in a given input image (e.g., relative to other objects depictedin the given input image).

To train a model to detect objects of the target class with a highdegree of accuracy (e.g., 95% or higher), training data can be generatedbased on a large number (e.g., thousands, or in some instances millions)of images (referred to as training images herein). In some systems, thedata used to train the model (referred to as training data herein) caninclude an indication of a region of each training image that includesan object (e.g., a bounding box), an indication of whether the object inthat region corresponds to the target class, and additional data (e.g.,mask data) that indicates the position (e.g., a pose, placement, ororientation, etc.) or a shape of the object. The collection of asignificant number of images that are suitable to be used for trainingthe model to detect and classify objects of a target class can take asignificant amount of time (e.g., months or in some instances, years).In addition, accurately determining labeled data for each image (e.g.,the region of each image that includes the object, the class associatedwith the object, and the mask data associated with the object) can takea further amount of time. In some systems, only highly trusted entitiesare relied on to accurately determine and provide labeled data for thetraining images. For example, in some systems, humans are relied on toprovide an indication of the region, the class, and the additional datafor objects depicted in each training image. However, in such systems,obtaining the labeled data for each training image can be overlyexpensive and can take a significant amount of time, as the highlytrusted entities providing the labeled data have to review thousands ifnot millions of images and determine and specify the labeled data foreach image.

In some instances, labeled data can be generated based on a smallernumber of training images (e.g., tens or hundreds) and/or based ondeterminations made by entities that are not highly trusted entities. Insuch systems, the machine learning model can initially be trained todetect and classify objects included in given input images with a lowdegree of accuracy (e.g., less than 95%). During deployment, the modelcan be retrained based on feedback provided for data determined based onone or more outputs of the model (e.g., the region of a given inputimage that includes a detected object and/or a class determined for thedetected object in the given input image, etc.). Eventually, the modelcan be retrained while deployed to detect and classify objects with ahigh degree of accuracy. However, retraining the model to detect andclassify objects with a high degree of accuracy can take an even largeramount of time and computing resources, in some instances, as time andcomputing resources are consumed to train the model and additional timeand resources are consumed to retrain the model during the interferencephase.

Embodiments of the present disclosure address the above and otherdeficiencies by providing a transfer learning technique to train anobject detection model to detect, in given input images, objectsassociated with a target class. A first machine learning model (alsoreferred to as a teacher model) may be trained (e.g., by a training datagenerator and/or a training engine) to detect one or more objectsdepicted in given input images. The objects depicted in the given inputimages may correspond to at least one of multiple (e.g., tens, hundreds,etc.) different classes, in some embodiments. The teacher model may betrained using first training data that may include a training inputincluding one or more images and a target output that includes labeleddata such as data associated with each object depicted in each of theset of images. In some embodiments, the data associated with each objectmay include an indication of a region of the one or more images thatincludes an object, an indication of a class (i.e., of the multipledifferent classes) associated with the object, and/or mask dataassociated with the object. Mask data refers to data (e.g., atwo-dimensional (2D) bit array) that indicates whether one or morepixels (or groups of pixels) for the image correspond to a certainobject. In some embodiments, the images and the data associated with theobjects depicted in the images may be obtained from a publicly availablerepository or database that includes a large number of different imagesand object data that can be used for training object detection machinelearning models. The teacher model may be trained, using the firsttraining data, to detect one or more objects depicted in a given set ofinput images and predict, for each detected object, at least mask dataassociated with the respective detected object. In some additionalembodiments, the teacher model may be trained to further predict aregion in an image of the given set of input images that includes thedepicted object (e.g., a bounding box) and/or a class (i.e., of themultiple different classes) that corresponds to the detected object.

Once the teacher model is trained using the first training data, thetrained teacher model may be used to generate second training data fortraining a second machine learning model (referred to as a studentmodel) to detect objects of a target class that are depicted in giveninput images. A set of images may be provided as input to the teachermodel. In some embodiments, each of the set of images may be selected(e.g., from a domain-specific or organization-specific repository ordatabase) to be used for generating the second training data fortraining the student model. Object data associated with objects detectedin each of the set of images provided as input to the teacher model maybe determined based on one or more obtained outputs of the teachermodel. In some embodiments, the object data may include mask dataassociated with each detected object. In some additional embodiments,the object data may further include, for each detected object, dataindicating an image region that includes the detected object and/or anindication of a class associated with the detected object.

Second training data including the one or more outputs of the teachermodel may be used to train the student model associated with the targetclass of objects. In particular, in some embodiments, the secondtraining data may include training input that includes the set of imagesand target output that includes the mask data associated with eachobject detected in the set of images. The training data generator mayobtain the mask data associated with each object using the obtainedoutputs of the teacher model, in some embodiments. The target output ofthe second training data may also include an indication of whether aclass associated with each object detected in the set of imagescorresponds to a target class. For example, as described above, one ormore outputs of the teacher model may include an indication of the classassociated with an object detected in a given input image that isincluded in the second training data. In some embodiments, the trainingdata generator may determine whether the class associated with eachdetected object corresponds to the target class based on the obtainedoutputs of the teacher model. The target output of the second trainingdata may further include ground truth data associated with each objectdetected in the set of images. The ground truth data may indicate aregion of the image (e.g., a bounding box) that includes a respectivedetected object. In some embodiments, the training data generator mayobtain the ground truth data from a database that includes an indicationof one or more bounding boxes associated with an image of the set ofimages (e.g., to use instead of bounding box data provided by theteacher model for higher accuracy). In some embodiments, the databasemay be a domain-specific or organization-specific database that includesthe set of images. Each of the bounding boxes associated with the imagemay be provided by an accepted bounding box authority entity or a userof the computing platform.

The second training data may be used to train the student model topredict, for a given input image, a bounding box associated with anobject detected in the given input image and mask data associated withthe object detected in the given input image. The student model may alsobe trained to predict whether a class associated with the objectdetected in the given input image corresponds to the target class. Asdescribe above, the teacher model may be trained to predict multipleclasses for an object detected in a given input image. As the studentmodel is trained to predict a specific class of objects (i.e., thetarget class) rather than multiple object classes, the student model mayprovide more accurate predictions than the teacher model.

In some instances, the trained student model may be a multi-head machinelearning model. For example, the trained student model may include afirst head for predicting a bounding box associated with an objectdetected in a given image, a second head for predicting a classassociated with the detected object, and a third head for predictingmask data associated with the detected object. An object detectionengine (e.g., of a computing device, of a cloud computing platform,etc.) may, in some instances, identify the head of the student modelthat corresponds to predicting mask data associated with a detectedobject (referred to as a mask head) and can update the student model toremove the identified head. After the mask head is removed from thestudent model, the updated student model may be used to predict abounding box and a class associated with an object detected in a giveninput image. By initially including the mask head in the student model,object detection and classification predictions of the updated studentmodel may be more accurate than an object detection model that istrained using training data that does not include mask data associatedwith objects depicted in provided training images. In addition, aninference speed associated with the student model may be significantlyimproved (e.g., by 10-20%) after the mask head is removed from thestudent model and a model size associated with the student mode may besignificantly reduced. Accordingly, in some instances, the updatedstudent model may be transmitted to an edge device and/or one or moreendpoint devices (e.g., a smart surveillance camera, an autonomousvehicle) over a network to be used for object detection.

Aspects and embodiments of the present disclosure provide a technique totrain an object detection model using transfer learning. Using a largeamount of images (i.e., from a publicly available repository ordatabase) depicting objects of various classes, training data may beused to train a teacher model to make predictions with a sufficientdegree of accuracy. Image data may be obtained from a domain-specific ororganization-specific repository or database and used as input to theteacher model to obtain predictions that can be used to train a studentmodel to increase a prediction accuracy for specific, focused, orparticular image classes. As such, the student model may be trained tomake predictions with a high(er) degree of accuracy (e.g., 95% orhigher) without obtaining labeled data for training from experts orother accepted authorities. In addition, embodiments of the presentdisclosure provide ability to provide an object detection model to beused at an edge device and/or an endpoint device (e.g., a smartsurveillance camera, an autonomous vehicle, etc.), where the objectdetection model is trained to detect objects of the target class withthe high degree of accuracy and also satisfy size constraints andinference speed conditions associated with the edge device and/or theendpoint device.

System Architecture

FIG. 1 is a block diagram of an example system architecture 100,according to at least one embodiment. The system architecture 100 (alsoreferred to as “system” herein) includes computing device 102, datastores 112A-N (collectively and individually referred to as datastore(s) 112), and server machine 130, server machine 140, and/or servermachine 150. In implementations, network 110 may include a publicnetwork (e.g., the Internet), a private network (e.g., a local areanetwork (LAN) or wide area network (WAN)), a wired network (e.g.,Ethernet network), a wireless network (e.g., an 802.11 network or aWi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE)network), routers, hubs, switches, server computers, and/or acombination thereof.

Computing device 102 may be a desktop computer, a laptop computer, asmartphone, a tablet computer, a server, or any suitable computingdevice capable of performing the techniques described herein. In someembodiments, computing device 102 may be a computing device of a cloudcomputing platform. For example, computing device 102 may be, or may bea component of, a server machine of a cloud computing platform. In suchembodiments, computing device 102 may be coupled to one or more edgedevices (not shown) via network 110. An edge device refers to acomputing device that enables communication between computing devices atthe boundary of two networks. For example, an edge device may beconnected to computing device 102, data stores 112A-N, server machine130, server machine 140, and/or server machine 150 via network 110, andmay be connected to one or more endpoint devices (not shown) via anothernetwork. In such example, the edge device can enable communicationbetween computing device 102, data stores 112A-N, server machine 130,server machine 140, and/or server machine 150 and the one or more clientdevices. In other or similar embodiments, computing device 102 may be,or may be a component of, an edge device. For example, computing device102 may facilitate communication between data stores 112A-N, servermachine 130, server machine 140, and/or server machine 150, which areconnected to computing device 102 via network 110, and one or moreclient devices that are connected to computing device 102 via anothernetwork.

In still other or similar embodiments, computing device 102 may be, ormay be a component of, an endpoint device. For example, computing device102 may be, or may be a component of, devices, such as, but not limitedto: televisions, smart phones, cellular telephones, personal digitalassistants (PDAs), portable media players, netbooks, laptop computers,electronic book readers, tablet computers, desktop computers, set-topboxes, gaming consoles, autonomous vehicles, surveillance devices, andthe like. In such embodiments, computing device 102 may be connected todata stores 112A-N, server machine 130, server machine 140 and/or servermachine 150 via network 110. In other or similar embodiments, computingdevice 102 may be connected to an edge device (not shown) of system 100via a network and the edge device of system 100 may be connected to datastores 112A-N, server machine 130, server machine 140 and/or servermachine 150 via network 110.

Computing device 102 may include a memory 104. Memory 104 may includeone or more volatile and/or non-volatile memory devices that areconfigured to store data. In some embodiments, computing device 102 mayinclude an object detection engine 151. Object detection engine 151 maybe configured to detect one or more objects depicted in an image (e.g.,image 106) and, in some embodiments, obtain data associated with the oneor more detected objects (e.g., object data 108). For example, objectdetection engine 151 can be configured to provide image 106 as input toa trained object detection model (e.g., a model 160) and determineobject data 108 associated with image 106 based on one or more outputsof the trained object detection model. It should be noted that althoughimplementations of the present disclosure are discussed in terms of anobject detection model, implementations may also be generally applied toany type of machine learning model. Further details regarding objectdetection engine 151 and the object detection model are provided herein.

As described above, computing device 102 may be, or may be a componentof, an endpoint device, in some embodiments. In such embodiments,computing device 102 may include an audiovisual component that cangenerate audio and/or visual data. In some embodiments, the audiovisualcomponent may include an image capture device (e.g., a camera) tocapture and generate an image 106, and generate image and/or video dataassociated with the generated image 106. In other or similarembodiments, computing device 102 may be, or may be a component of, anedge device, as described above. In such embodiments, computing device102 can receive image 106 from an endpoint device that includes theaudiovisual component (i.e., via network 110 or another network). Asalso described above, computing device 102 may be, or may be a componentof, a server machine (e.g., for a cloud computing platform), in someembodiments. In such embodiments, computing device 102 may receive image106 from an endpoint device that includes the audio visual componentand/or an edge device that is connected to the endpoint device (i.e.,via network 110 or another network).

In some implementations, data store 112A-N is a persistent storage thatis capable of storing content items (e.g., images) and data associatedwith the stored content items (e.g., object data) as well as datastructures to tag, organize, and index the content items and/or theobject data. Data store 112 may be hosted by one or more storagedevices, such as main memory, magnetic or optical storage based disks,tapes or hard drives, NAS, SAN, and so forth. In some implementations,data store 112 may be a network-attached file server, while in otherembodiments data store 112 may be some other type of persistent storagesuch as an object-oriented database, a relational database, and soforth, that may be hosted by computing 102 or one or more differentmachines coupled to the computing device 102 via network 110.

As illustrated in FIG. 1 , system 100 may include multiple data stores112, in some embodiments. In some embodiments, a first data store (e.g.,data store 112A) may be configured to store data that is accessible onlyto computing device 102, server machine 130, server machine 140, and/orserver machine 150. For example, data store 112A may be or include adomain-specific or organization-specific repository or data base. Insome embodiments, computing device 102, server machine 130, servermachine 140, and/or server machine 150 may only be able to access datastore 112A via network 110, which may be a private network. In other orsimilar embodiments, data stored at data store 112A may be encrypted andmay be accessible to computing device 102, server machine 130, servermachine 140, and/or server machine 150 via an encryption mechanism(e.g., a private encryption key, etc.). In additional or alternativeembodiments, a second data store (e.g., data store 112B) may beconfigured to store data that is accessible to any device that isaccessible to data store 112B via any network. For example, data store112B may be or include a publicly accessible repository or database.Data store 112B may be a publicly accessible data store that isaccessible to any device via a public network, in some embodiments. Inadditional or alternative embodiments, system 100 may include a datastore 112 that is configured to store first data that is accessible onlyto computing device 102, server machine 130, server machine 140, and/orserver machine 150 (e.g., via private network 110, via an encryptionmechanism, etc.) and second data that is accessible to devices that areconnected to data store via another network (e.g., a public network). Inyet additional or alternative embodiments, system 100 may only include asingle data store 112 that is configured to store data that isaccessible only to computing device 102, server machine 130, servermachine 140, and/or server machine 150 (e.g., via private network 110,via an encryption mechanism, etc.). In such embodiments, data store 112may store data that is retrieved (e.g., by computing device 102,training data generator 131, training engine 141, etc.) from a publiclyaccessible data store.

Server machine 130 may include a training data generator 131 that iscapable of generating training data (e.g., a set of training inputs anda set of target outputs) to train ML models 160A-N. Training data may bebased on images stored at data store that is or includes adomain-specific or organization-specific repository or database, (e.g.,data store 112A, etc.), or a private portion of a data store 112, and/orimages stored at a data store that is or includes a publicly accessiblerepository or database (e.g., data store 112B, etc.), or a publiclyaccessible portion of data store 112. For example, training datagenerator 131 may generate training data for a teacher machine learningmodel (e.g., a teacher object detection model) based on images stored atdata store 112B, a publicly accessible portion of data store 112, orretrieved from a publicly accessible data store (not shown). In anotherexample, training data generator 131 may generate training data for astudent machine learning model (e.g., a student object detection model)based on images stored at data store 112A, a private portion of datastore 112, or from a single, private data store 112, as described above,and based one or more outputs of the teacher object detection model.Further details regarding generating training data for a teacher objectdetection model and a student object detection model are provided withrespect to FIG. 2 .

Server machine 140 may include a training engine 141. Training engine141 may train a machine learning model 160A-N using the training datafrom training set generator 131. The machine learning model 160A-N mayrefer to the model artifact that is created by the training engine 141using the training data that includes training inputs and correspondingtarget outputs (correct answers for respective training inputs). Thetraining engine 141 may find patterns in the training data that map thetraining input to the target output (the answer to be predicted), andprovide the machine learning model 160A-N that captures these patterns.The machine learning model 160A-N may be composed of, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine(SVM or may be a deep network, i.e., a machine learning model that iscomposed of multiple levels of non-linear operations). An example of adeep network is a neural network with one or more hidden layers, andsuch a machine learning model can be trained by, for example, adjustingweights of a neural network in accordance with a backpropagationlearning algorithm or the like. For convenience, the remainder of thisdisclosure will refer to the implementation as a neural network, eventhough some implementations might employ an SVM or other type oflearning machine instead of, or in addition to, a neural network. Insome embodiments, the training data may be obtained by training datagenerator 131 hosted by server machine 130. For example, training engine141 may obtain first training data to train a teacher object detectionmodel and second training data to train a student object detection modelfrom training set generator 131. Further details regarding training anobject detection model (e.g., model 160A-N) are provided with respect toFIG. 2 .

Server 150 may include an object detection engine 151 that provides oneor more images as input to a trained machine learning model 160A-N toobtain one or more outputs. In some embodiments, the one or more imagesbe stored at data store 112 or a private portion of data store 112, asdescribed above. For example, a trained machine learning model 160A maybe a trained teacher object detection model. In such example, objectdetection engine 151 may provide the one or more images as input totrained machine learning model 160A to obtain one or more first outputs.Training data generator 131 may use the one or more first outputs ofmachine learning model 160A to generate training data to train a studentobject detection model, in accordance with embodiments provided herein.In another example, a trained machine learning model 160B can be atrained student object detection model. In such example, objectdetection engine 151 may provide one or more images 106 obtained bycomputing device 102 as input to trained machine learning model 160B toobtain one or more second outputs. Object detection engine 151 may usethe one or more second outputs to detect an object depicted in the oneor more images 106 and determine object data 108 associated with the oneor more detected objects. Further details regarding object detectionengine 151 are provided with respect to FIG. 3 .

In some implementations, computing device 102, data stores 112, and/orserver machines 130-150, may be one or more computing devices computingdevices (such as a rackmount server, a router computer, a servercomputer, a personal computer, a mainframe computer, a laptop computer,a tablet computer, a desktop computer, etc.), data stores (e.g., harddisks, memories, databases), networks, software components, and/orhardware components that may be used to enable object detection based onan image (e.g., image 106). It should be noted that in some otherimplementations, the functions of computing device 102, server machines130, 140, and/or 150 may be provided by a fewer number of machines. Forexample, in some implementations server machines 130 and 140 may beintegrated into a single machine, while in other implementations servermachines 130, 140, and 150 may be integrated into multiple machines. Inaddition, in some implementations one or more of server machines 130,140, and 150 may be integrated into computing device 102. In general,functions described in implementations as being performed by computingdevice 102 and/or or server machines 130, 140, 150 may also be performedon one or more edge devices (not shown) and/or client devices (notshown), if appropriate. In addition, the functionality attributed to aparticular component may be performed by different or multiplecomponents operating together. Computing device 102 and/or servermachines 130, 140, 150 may also be accessed as a service provided toother systems or devices through appropriate application programminginterfaces.

FIG. 2 is a block diagram of an example training data generator 131 andan example training engine 141, according to at least one embodiment.Training data generator 131 may include a teacher model training datagenerator 210 and a student model training data generator 220. Trainingengine 141 may include a teacher model training module 230 and a studentmodel training module 232. As described previously, training datagenerator 131 may reside at a server machine, such as server machine 130of FIG. 1 , that is a part of or separate from computing device 102.Training engine 141 may reside at server machine 130 or another servermachine, such as server machine 140, that is a part of or separate fromcomputing device 102.

In some embodiments, teacher model training data generator 210 may beconfigured to generate training data for training a teacher objectdetection model (e.g., model 160A) and student model training datagenerator 220 may be configured to generate training data for training astudent object detection model (e.g., model 160B). As illustrated inFIG. 2 , training data generator 131 may be connected to data store 250.Data store 250 may be configured to store data that is used by teachermodel training data generator 210 to generate training data for trainingthe teacher object detection model. For example, data store 250 may beconfigured to store one or more training images 252, and for eachtraining image 252, training image region of interest (ROI) data 254,training image mask data 256, and/or training image object data 258associated with the training image 252. In some embodiments, eachtraining image 252 may depict an object that is associated with aparticular class of a set of multiple distinct object classes. In anillustrative example, a training image 252 may depict a first objectthat corresponds to a person class and a second object that correspondsto an animal class (e.g., a dog class).

Training image ROI data 254 can indicate each region of a respectivetraining image 252 that depicts a respective object. In someembodiments, training image ROI data 254 can correspond to a boundingbox or another bounding shape (e.g., a spheroid, an ellipsoid, acylindrical shape, etc.) that indicates a region of the training image252 that depicts the respective object. In accordance with the previousexample, training image ROI data 254 associated with the exampletraining image 252 may include a first bounding box that indicates afirst region of training image 252 that depicts the first object and asecond bounding box that indicates a second region of training image 252that depicts the second object.

Training image mask data 256 can refer to data (e.g., a two-dimensional(2D) bit array) that indicates whether one or more pixels (or groups ofpixels) for a respective training image 252 corresponds to a particularobject. In accordance with the previous example, training image maskdata 256 associated with the example training image 252 may include anindication of a first group of pixels that corresponds to the firstobject and an indication of a second group of pixels that corresponds tothe second object.

Training image object data 258 may refer to data that indicates one ormore characteristics associated with each object depicted in arespective training image 252. In some embodiments, training imageobject data 258 may include data that indicates a class (i.e., ofmultiple classes) associated with a depicted object. For example,training image object data 258 associated with the example trainingimage 252 may include data that indicates the first object depicted intraining image 252 is associated with a first class (e.g., a personclass) and the second object depicted in the training image 252 isassociated with a second class (e.g., an animal class). In additional oralternative embodiments, training image object data 258 can include datathat indicates other characteristics associated with each depictedobject, such as a position (e.g., an orientation, etc.) or a shape ofthe object.

In some embodiments, training image 252 may be included in a collectionof images that may be used to train object detection models. Forexample, training image 252 may be included in a collection of publiclyaccessible images that may be retrieved from a publicly accessible datastore (e.g., data store 112B), or a publicly accessible portion of adata store (e.g., data store 112) and used to train an object detectionmodel. In some embodiments, each of the collection of images may beassociated with image data, which may also be included at the publiclyaccessible data store or the publicly accessible portion of a datastore. In some embodiments, each of the collection of images and theimage data associated with each of the collection of images may beprovided by one or more users of an object detection platform. Forexample, a user of the object detection platform may provide (i.e., viaa respective client device associated with the user) an image depictingone or more objects. The user may also provide (i.e., via a graphicaluser interface of the respective client device) an indication of ROIdata, mask data, and object data associated with each object depicted inthe provided image. In another example, a first user of the objectdetection platform may provide (i.e., via a first client deviceassociated with the first user) an image depicting one or more objectsand a second user may provide (i.e., via a graphical user interface of asecond client device associated with the second user) an indication ofROI data, mask data, and object data associated with each objectdepicted in the image provided by the first user. In some embodiments,data retrieval module 212 of teacher model training data generator 210may retrieve training image 252 from the publicly accessible data store,or the publicly accessible portion of a data store, to be used to traina teacher object detection model, in accordance with embodimentsprovided herein.

In some embodiments, data store 250 may correspond to a publiclyaccessible data store, such as data store 112B, described with respectto FIG. 1 . In other or similar embodiments, data store 250 maycorrespond to a publicly accessible portion of data store 112A. Dataretrieval module 212 may retrieve training image 252, training image ROIdata 254, training image mask data 256, and/or training image objectdata 258 from data store 250 (i.e., from data store 112A or data store112B). In other or similar embodiments data store 250 may correspond toa data store 112 that is only accessible via a private network and/orvia an encryption mechanism (e.g., data store 112A). In suchembodiments, data retrieval module 212 may retrieve training image 252,training image ROI data 254, training image mask data 256, and/ortraining image object data 258 from a publicly accessible data store(not shown) and store In such embodiments, data retrieval module 212 mayretrieve training image 252, training image ROI data 254, training imagemask data 256, and/or training image object data 258 at data store 250.

Training data generator module 214 may generate training data to be usedto train the teacher object detection model responsive to data retrievalmodule 212 retrieving training image 252, training image ROI data 254,training image mask data 256, and/or training image object data 258. Thetraining data may include a set of training inputs and a set of targetoutputs, in some embodiments. The set of training inputs may include oneor more training images 252 retrieved by data retrieval module 212, asdescribed above. In some embodiments, training data generator module 214may apply one or more image transformations to the one or more trainingimages 252 retrieved by data retrieval module 212. For example, atraining image 252 retrieved by data retrieval module 212 may beassociated with a particular amount of image noise. Training datagenerator module 214 may apply one or more image transformations to theretrieved training image 252 to generate a modified training image. Themodified training image may include a different amount of image noise(e.g., a smaller amount of image noise) than the retrieved trainingimage 252. Training data generator module 214 may include the modifiedtraining image in the set of training images 252, in accordance withembodiments described above. The set of target outputs may includetraining image ROI data 254, training image mask data 256, and/ortraining image object data 258. Responsive to generating the set oftraining inputs and the set of target outputs, training data generatormodule 214 may generate a mapping between the set of training inputs andthe set of target outputs to generate teacher model training data 272.

In some embodiments, teacher model training data generator 210 may storethe teacher model training data 272 at data store 270. Data store 270may be a data store that is or includes a domain-specific ororganization-specific repository or database (e.g., data store 112A), ora private portion of a data store 112, that is accessible to computingdevices via a private network and/or via an encryption mechanism. Datastore 270 may be accessible to training data generator 131 and/ortraining engine 141, in accordance with embodiments described withrespect to FIG. 1 . In additional or alternative embodiments, teachermodel training data generator 210 may provide the generated mapping toteacher model training module 230 of training engine 141 to train theteacher object detection model.

Responsive to obtaining training data 272 (i.e., from teacher modeltraining data generator 210 or from data store 270), teacher modeltraining engine 230 may use the training data 272 to train the teacherobject detection model. In some embodiments, the teacher objectdetection model may be trained to detect, for a given input image, oneor more objects of the multiple classes depicted in the given inputimage and predict, for each detected object, mask data and/or ROI dataassociated with the detected object. In some embodiments, the teacherobject detection model may also be trained to predict, for each objectdetected in a given input image, object data (e.g., an object class,other characteristic data associated with the object, etc.). In other orsimilar embodiments, the teacher object detection model may be trainedto detect, for a given input image, one or more objects of a singleclass depicted in the given input image and predict, for each detectedobject, mask data, ROI data, object class data, and/or other objectcharacteristic data associated with the detected object. In someembodiments, responsive to training the teacher object detection model,training engine 141 may store the trained teacher object detection modelat data store 270 as teacher model 274. Student model training datagenerator 220 may use trained teacher model 274 to generate trainingdata to train the student object detection model, in accordance withembodiments provided herein.

Data store 270 may store one or more training images 276 that are to beused to train the student object detection model to detect objectsassociated with a target class. For example, a target class maycorrespond to a people class. In such example, one or more trainingimages 276 at data store 270 may depict one or more objects associatedwith the people class. In some embodiments, each object depicted in arespective training image 276 may be associated with a distinctcharacteristic (e.g., a person in a distinct environment, a persondisposed in a distinct position, etc.). Accordingly, the one or moretraining images 276 may be used to train the student object detectionmodel to detect the objects that are associated with the people classand also associated with distinct characteristics. In another example,one or more training image 276 at data store 270 may depict one or moreobjects that are not associated with the people class but correspond toone or more characteristics that are similar to objects associated withthe people class (e.g., an object depicted in a training image 276 isassociated with a similar position or environment associated with anobject of the people class). Accordingly, the one or more trainingimages 276 may be used to train the student object detection model todetect objects that correspond to one or more characteristics associatedwith the people class, but are not associated with the people class.

Teacher inference module 222 may retrieve one or more training images276 from data store 270 and provide the one or more training images 276as input to trained teacher model 274. Teacher output module 224 mayobtain one or more outputs of the trained teacher model 274 and maydetermine, from the one or more obtained outputs, object data associatedwith each input training image 276. In some embodiments, the determinedobject data for a respective training image 274 may include output imageROI data 278, output image mask data 280, and/or output imagecharacteristic data 282. The output image ROI data 278 associated with arespective training image 276 may a region of the respective trainingimage 276 that depicts a particular object. The output image mask data280 may indicate mask data associated the particular object. The outputimage characteristic data 282 may indicate one or more characteristicsassociated with the particular object, such as a class of the object, aposition of the object, a shape of the object, and so forth. In someembodiments, teacher output module 224 may store output image ROI data278, output image mask data 280, and/or output image characteristic data282 at data store 270.

Training data generator module 226 may generate training data to be usedto train the student object detection model to detect, for a given inputimage, one or more objects associated with a target class. The trainingdata may include a set of training inputs and a set of target outputs,in some embodiments. The set of training inputs may include one or moretraining images 276 that were provided as input to trained teacher model274, as described above. The set of target outputs may include at leastoutput image mask data 280 determined from one or more outputs oftrained teacher model 274. In some embodiments, the set of targetoutputs may include output image mask data 280 and output imagecharacteristic data 282.

In some embodiments, training data generator module 226 may generateupdated image characteristic data based on the output imagecharacteristic data 282 determined from the one or more outputs oftrained teacher model 274. For example, a training image 276 that isprovided as input to trained teacher model 274 may depict a first objectassociated with a first class (e.g., a people class, etc.) and a secondobject associated with a second class (e.g., an animal class, etc.).Teacher output module 224 may obtain one or more outputs from thetrained teacher model 274 that indicates the first and second objectsdetected in the given input image and output image characteristic data282 that indicates the first object is associated with the first classand the second object is associated with the second class. Training datagenerator module 226 may determine whether the first class and/or thesecond class correspond to the target class and may generate updatedimage object data based on the determination. For example, if the targetclass is a people class, training data generator module 226 maydetermine that the first class corresponds to the target class andgenerate updated image characteristic data to indicate that the firstobject corresponds to the target class. Training data generator module226 may also determine that the second class does not correspond to thetarget class and may generate updated image characteristic data toindicate that the second object does not correspond to the target class.Training data generator module 226 may include the updated object datain the set of target outputs instead of output image characteristic data282, in some embodiments.

In some embodiments, the set of target outputs may additionally includeimage ground truth data 284. Image ground truth data 284 may indicate aregion of a respective training image 276 that includes an objectdetected by trained teacher model 274. For example, ground truth data284 may include an indication of one or more bounding boxes associatedwith training images 276 that are obtained from an accepted bounding boxauthority entity or a user of a computing device or an object detectionplatform. In some embodiments, ground truth data 284 may be obtainedfrom the accepted bounding box authority entity or the user before orafter the one or more training images 276 are provided as input to thetrained teacher model 274. In an illustrative example, image groundtruth data 284 may correspond to output image ROI data 278, except thata bounding box of image ground truth data 284 may more accuratelyidentify a region of an image 276 that depicts a particular object thana bounding box of output image ROI data 278. In some additional oralternative embodiments, image ground truth data 284 may indicate aclass of the object detected by trained teacher model 274. In someembodiments, the set of target outputs may include the class of theobject indicated by image ground truth data 284 instead of the class ofthe object indicated by output image characteristic data 282, asdescribed above. In some embodiments, training data generate module 226may generate updated image characteristic data based on the object classindicated by image ground truth data 284, in accordance with previouslydescribed embodiments.

Responsive to generating the set of training inputs and the set oftarget outputs for a respective training image 276, training datagenerator module 226 may generate a mapping between the set of traininginputs and the set of target outputs to generate student model trainingdata 286. In some embodiments, student model training data generator 220may store the student model training data 286 at data store 270. Inother or similar embodiments, student model training data generator 220may transmit the student model training data 286 to training engine 141.Responsive to obtaining training data 286 (i.e., from student modeltraining data generator 220 or from data store 270), student modeltraining module 232 may use the training data 286 to train the studentobject detection model. The student object detection model may betrained to detect, for a given input, one or more objects of the targetclass depicted in the given input image and predict, for each detectedobject, mask data, ROI data, and/or characteristic data associated withthe detected object. In some embodiments, training engine 141 mayprovide the trained student object detection model to an objectdetection engine, such as object detection engine 151 of FIG. 1 .

FIG. 3 is a block diagram of an example object detection engine 310,according to at least one embodiment. In some embodiments, objectdetection engine 310 may correspond to object detection engine 151,described with respect to FIG. 1 . As illustrated in FIG. 3 , objectdetection engine 310 may include an input image component 312, an objectdata component 314, a model head component 316 and/or a model updatecomponent 318. In some embodiments, object detection engine 310 may becoupled to a memory 320. In some embodiments, object detection engine310 may reside at computing device 102. In such embodiments, memory 320may correspond to memory 104 described with respect to FIG. 1 . In otheror similar embodiments, object detection engine 310 may reside at server150. In such embodiments, memory 320 may correspond to memory at a datastore (e.g., data store 112), memory 104, or memory of another memorydevice associated with system 100.

Input image component 312 may be configured to obtain an image (e.g.,image 106) and provide the obtained image as input to a trained objectdetection model 322 stored at memory 320. In some embodiments, trainedobject detection model 322 can correspond to a student object detectionmodel that is trained by training engine 141 using training datagenerated by training data generator 131, as described with respect toFIGS. 1 and 2 . In other or similar embodiments, trained objectdetection model 322 may correspond to another trained object detectionmodel that is not trained by training engine 141 using training datagenerated by training data generator 131.

As described with respect to FIG. 1 , computing device 102 may be acomputing device of a cloud computing platform, in some embodiments. Insuch embodiments, computing device 102 may be coupled to one or moreedge devices (e.g., edge device 330), which are each coupled to one ormore endpoint devices (e.g., endpoint devices 332A-N (collectively andindividually referred to as endpoint device 332 herein)), as illustratedin FIG. 3 . In some embodiments, an audiovisual and/or sensorcomponent(s) of an endpoint device 332 may generate image 106, asdescribed above, and transmit image 106 to edge device 330 (e.g., via anetwork). Edge device 330 may transmit the received image 106 tocomputing device 102 (e.g., via network 110). In such embodiments,computing device 102 may transmit image 106 to input image component 312(e.g., via network 110 or a BUS of computing device 102). In other orsimilar embodiments, computing device 102 may be, or may be a componentof, edge device 330, as also described above. In such embodiments, edgedevice 330 may receive image 106 from an endpoint device 332 (e.g., viaa network) and may transmit image 106 to input image component 312(e.g., via network 110 or a BUS of computing device 102). In yet otheror similar embodiments, computing device 102 may be, or may be acomponent of, one or more endpoint devices 332A-N, as previouslydescribed. In such embodiments, the one or more endpoint devices 332A-Nmay generate image 106 and transmit image 106 to input image component312 (e.g., via a network, network 110 or a BUS of computing device 102).

In response to receiving image 106, input image component 312 mayprovide image 106 as input to trained object detection model 322, insome embodiments. In other or similar embodiments, input image component312 may apply one or more image transformations (e.g., to reduce anamount of noise included in image 106), as described above, to generatea modified image and provide the modified image as input to trainedobject detection model 322. Object data component 314 may obtain one ormore outputs of trained object detection model 322 and may determineobject data 108 based on the one or more obtained outputs. In someembodiments, the object data 108 determined based on the one or moreobtained outputs can correspond to one or more objects detected in thegiven input image 106 (or the modified input image). For example, objectdata 108 may include an indication of a region of image 106 thatincludes a detected object (e.g., a bounding box). In some additional oralternative embodiments, object data 108 may further include mask dataassociated with the detected object. In some additional or alternativeembodiments, object data 108 may further include data that indicates aclass and/or one or more characteristics associated with the detectedobject.

In some embodiments, the one or more outputs of trained object detectionmodel 322 may include an indication of multiple regions of image 106 andan indication of a level of confidence that each region includes adetected object. Object data component 314 may determine that aparticular region of image 106 includes a detected object by determiningthat a level of confidence associated with the particular region ofimage 106 satisfies a level of confidence criterion (e.g., the level ofconfidence exceeds a threshold value, etc.). Responsive to determiningthat the particular region of image 106 satisfies the level ofconfidence criterion, object data component 314 may include theindication of the particular region of image 106 with object data 108 inmemory 320. In additional or alternative embodiments, the one or moreoutputs of trained object detection model 322 may include multiple setsof mask data and an indication of a level of confidence that each set ofmask data is associated with the detected object. Responsive todetermining that a particular set of mask data satisfies a level ofconfidence criterion, object data component 314 may include theindication of the particular mask data with object data 108 in memory320. In yet additional or alternative embodiments, the one or moreoutputs of trained object detection model 322 may include multipleclasses and/or characteristics and an indication of a level ofconfidence that each class and/or characteristic corresponds to adetected object. Responsive to determining that a particular classand/or characteristic satisfies a level of confidence criterion, objectdata component 314 may include the indication of the particular classand/or characteristic with object data 108 in memory 320.

In some embodiments, object data component 314 may determine, based onobject data 108, whether an object detected in the given input image 106corresponds to the target class. For example, in some embodiments,object data 108 may indicate a class associated with a detected object,as described above. Object data component 312 may compare the indicatedclass to the target class to determine whether the detected objectcorresponds to the target class. In another example, object data 108 mayinclude data that indicates whether the detected object corresponds to atarget class, in accordance with previously described embodiments. Insuch example, object data component 312 may determine whether thedetected object corresponds to the target class based on the includeddata. In some embodiments, object data component 314 may update objectdata 108 to include an indication of whether the detected objectcorresponds to the target class. Object data component 314 may transmitobject data 108 to computing device 102. In some embodiments, objectdata component 312 may additionally or alternatively transmit, tocomputing device 102 a notification indicating whether a detected objectcorresponds to the target class.

As described above, in some embodiments, trained object detection model322 (e.g., the trained student object detection model of FIG. 2 ), maybe trained to predict ROI data, image mask data, and/or imagecharacteristic data associated with a given input image. In suchembodiments, trained object detection model 322 may be a multi-headmodel, where each head of the multi-head model is used to predict aparticular type of data associated with an object detected in a giveninput image. For example, trained object detection model 322 may includea first head that corresponds to predicting ROI data associated with adetected object, a second head that corresponds to predicting mask dataassociated with a detected object, and/or a third head that correspondsto predicting characteristic data associated with a detected object,such as a class of the detected object. As described above, in someembodiments, a significant number of training images (e.g., trainingimages 276) may be used to train the object detection model. Forexample, in some systems, hundreds, thousands, or, in some instances,millions of images may be used to train the object detection model. Inview of the above, the multi-head object detection model 322 that istrained on a significant number of images may, in some embodiments,consume a significant amount of system resources (e.g., memory space,processing resources, etc.). In some embodiments, object detectionengine 310 may remove one or more heads (e.g., a mask head) of thetrained, multi-head object detection model 322 to reduce the amount ofsystem resources consumed by object detection model 322 before and/orduring inference.

FIG. 4A depicts an example trained, multi-head object detection model322, according to at least one embodiment. As illustrated in FIG. 4A,model 322 may include at least a ROI head 412 and a mask head 414. Itshould be noted that although model 322, as depicted in FIG. 4A, may besimilar to a neural network, embodiments of this disclosure may beapplied to any type of machine learning model. Input image component 312may provide image 106 as input to model 322, in accordance withpreviously described embodiments. The input image 106 may be provided toboth ROI head 412 and mask head 414 of model 322. Model 322 may provideone or more outputs based on the given input image 106, as describedabove. In some embodiments, the provided output can include a ROI headoutput 416 and a mask head output 418. The ROI head output 416 may beprovided based on inference performed in accordance with ROI head 412.The mask head output 418 may be provided based on inference performed inaccordance with mask head 414.

Referring back to FIG. 3 , in some embodiments, model head component 316and model update component 318 of object detection engine 310 may removea mask head of object detection model 322. For example, model headcomponent 316 may identify one or more heads of model 322 (e.g., maskhead 414) that correspond to providing a particular output (e.g.,predicting mask data) associated with a given input image. Responsive tomodel head component 316 identifying the corresponding heads of model322, model update component 318 may update model 322 to remove the oneor more identified heads. FIG. 4B depicts an updated trained objectdetection model 324 that is updated to remove mask head 414, accordingto at least one embodiment. As illustrated in FIG. 4B, model updatecomponent 318 may remove the mask head 414 of model 322 to generateupdated model 324. Accordingly, model 324 may provide ROI head output416 and may not provide mask head output 418, in some embodiments. Itshould be noted that although FIG. 3 and FIGS. 4A-4B include embodimentsdirected to removing mask head 414 from model 322, embodiments of thepresent disclosure may be applied to removing any head of model 322.

Referring back to FIG. 3 , in some embodiments, object detection engine310 may transmit the updated object detection model 324 to computingdevice 102 (e.g., via network 110 or a bus of computing device 102). Asdescribed above, computing device 102 may be, or may be a component of,a cloud computing platform, in some embodiments. In such embodiments,computing device 102 may transmit updated object detection model 324 toedge device 330 (e.g., via network 110). Edge device 330 may use updatedobject detection model 324 to perform object detection based on images106 generated by endpoint devices 332A-N, in some embodiments. In otheror similar embodiments, edge device 330 may transmit updated objectdetection model 324 to endpoint devices 332A-N. As also described above,computing device 102 may be, or may be a component of, edge device 330.In such embodiments, edge device 330 may use updated object detectionmodel 324 to perform object detection and/or may transmit updated objectdetection model 324 to endpoint devices 332A. As also described above,computing device 102 may be, or may be a component of, one or moreendpoint devices 332A-N. In such embodiments, one or more of endpointdevices 332A-N may use updated object detection model 324 to performobject detection, in accordance with embodiments described above.

FIGS. 5A-B and 6 are flow diagrams of example methods 500, 550 and 600,respectively, which are related to training an object detection model,according to at least some embodiments. In at least one embodiment,methods 500, 550 and/or 600 may be performed by computing device 102,server machine 130, server machine 140, server machine 150, one or moreedge devices, one or more endpoint devices, or some other computingdevice, or a combination of multiple computing devices. Methods 500, 550and/or 600 may be performed by one or more processing units (e.g., CPUsand/or GPUs), which may include (or communicate with) one or more memorydevices. In at least one embodiment, methods 500, 550 and/or 600 may beperformed by multiple processing threads (e.g., CPU threads and/or GPUthreads), each thread executing one or more individual functions,routines, subroutines, or operations of the method. In at least oneembodiment, processing threads implementing methods 500, 550 and/or 600may be synchronized (e.g., using semaphores, critical sections, and/orother thread synchronization mechanisms). Alternatively, processingthreads implementing methods 500, 550 and/or 600 may be executedasynchronously with respect to each other. Various operations of methods500, 550 and/or 600 may be performed in a different order compared withthe order shown in FIGS. 5A-B and 6. Some operations of the methods maybe performed concurrently with other operations. In at least oneembodiment, one or more operations shown in FIGS. 5A-B and 6 may notalways be performed.

FIG. 5A illustrates a flow diagram of an example method 500 of traininga machine learning model to detect objects of a target class, accordingto at least one embodiment. In some embodiments, one or more operationsof method 500 may be performed by one or more components or modules oftraining data generator 131, described herein. Processing unitsperforming method 500 may identify, at block 510, a first set of imagesincluding multiple objects of multiple classes. In some embodiments,processing units may obtain the first set of images from data store 270,as described previously.

At block 512, processing units performing method 500 may provide thefirst set of images as input to a first machine learning model. Thefirst machine learning model may be a trained teacher object detectionmodel that is trained to detect, for a given input image, one or moreobjects of multiple classes depicted in the given image. The trainedteacher object detection model may also be trained to predict, for eachof the one or more detected objects, mask data and, in some embodiments,ROI data associated with the respective detected object. In additionalor alternative embodiments, the trained teacher object detection modelmay be trained to predict, for each detected object, a particular classof the multiple classes, associated with a respective detected object.At block 514, processing units performing method 500 may determine, fromone or more outputs of the first machine learning model, object dataassociated with the first set of images. The object data for eachrespective image of the first set of images may include mask dataassociated with each object detected in the respective image.

At block 516, processing units performing method 500 may train a secondmachine learning model to detect objects of a target class in a secondset of images using the first set of images and a portion of the objectdata determined from the one or more outputs of the first machinelearning model. The second machine learning model may be a studentobject detection model. Processing units may train the student objectdetection model using a training input and a target output. The traininginput may include the first set of images (i.e., that were provided asinput to the trained teacher object detection model). The target outputmay include the mask data associated with each detected object in thefirst set of images that is included in the one or more outputs of theteacher object detection model. The target output may also include anindication of whether a class associated with each object detected inthe first set of images corresponds to the target class. In someembodiments, processing units may determine whether the particular classassociated with each object detected in the first set of imagescorresponds to the target class. The target output may include anindication of whether the particular class corresponds to the targetclass. In another embodiment, the target output may include anindication of the particular class that associated with each objectdetected in the first set of images.

The target output may also include ground truth data associated witheach object detected in the first set of images, in some embodiments. Asdescribed above, the ground truth data associated with each detectedobject may indicate a region of an image that includes a respectivedetected object. Processing units may identify the ground truth datausing a database (e.g., at data store 270) that includes an indicationof one or more ROIs (e.g., bounding boxes) associated with the image, inaccordance with previously described embodiments. Each of the ROIsincluded at the database may be provided by an accepted ROI authority ora user of a platform. In some embodiments, processing units may alsoidentify a class associated with each object detected in the first setof images using the database.

In some embodiments, the trained second machine learning model may be amulti-head machine learning model, as described above. In someembodiments, processing units may identify one or more heads of thesecond machine learning model that correspond to predicting mask datafor a given input image and may update the second machine learning modelto remove the one or more identified heads. In some embodiments,processing units may provide the second set of images as input to thesecond machine learning model and obtain one or more outputs of thesecond machine learning model. Processing units may determine, from theone or more obtained outputs, additional object data associated witheach of the second set of images. The additional object data may includean indication of a region of a respective image (e.g., a bounding box)that includes an object detected in the respective image and a classassociated with the detected object, in some embodiments.

FIG. 5B illustrates a flow diagram of an example method 550 of using amachine learning model that is trained to detect objects of a targetclass, according to at least one embodiment. In some embodiments, one ormore operations of method 550 may be performed by one or more componentsor modules of object detection engine 151, described herein. Processingunits performing method 550 may provide, at block 552, a set of currentimages as input to a first machine learning model. In some embodiments,the set of current images may be generated by an audiovisual component(e.g., a camera) at or coupled to an endpoint device, an edge device, ora server, as described above.

The first machine learning model may be trained to detect objects of atarget class in a given set of images. In some embodiments, the firstmachine learning model may correspond to the student object detectionmodel, as described above. In some embodiments, the first machinelearning model may be trained in accordance with previously describedembodiments. For example, the first machine learning model may betrained using a training input including a set of training images and atarget output for the training input. The target output may include, foreach respective training image of the set of training images, groundtruth data associated with each object depicted in the respectivetraining image. The ground truth data may indicate a region of therespective training image that includes a respective object. In someembodiments, the ground truth data may be obtained using a databaseincluding an indication of one or more bounding boxes associated withthe set of training images. Each of the one or more bounding boxes maybe provided by an accepted bounding box authority entity and/or a userof a platform.

The target output may also include mask data associated with each objectdepicted in the respective training image. The mask data may be obtainedbased on one or more outputs of a second machine learning model. In someembodiments, the second machine learning model may correspond to theteacher model, described herein. For example, the set of training imagesmay be provided as input to the second machine learning model. Thesecond machine learning model may be trained to detect, for a giveninput image, one or more objects of at least one of multiple classesdepicted in the given input image and to predict, for each of the one ormore detected objects, at least mask data associated with the respectivedetected object. Object data may be determined from one or more outputsof the second machine learning model, as described above. The objectdata for each respective training image may include mask data associatedwith each object detected in the respective image. The target output mayalso include an indication of whether a class associated with eachobject depicted in the respective training image corresponds to thetarget class, in accordance with previously described embodiments.

Processing units performing method 550 may obtain, at block 554, one ormore outputs of the first machine learning model. Processing unitsperforming method 550 may determine, at block 556, object dataassociated with each of the set of current images based on the one ormore obtained outputs. In some embodiments, the determined object datafor each respective image of the set of current images may include anindication of a region of the respective image that includes an objectdetected in the respective image and an indication of whether thedetected object corresponds to the target class. In some embodiments,the object data may further include mask data associated with the objectdetected in the respective image. In some embodiments, the object dataassociated with each of the set of images may be determined byextracting one or more sets of object data from the one or more outputsof the first machine learning model. Each of the set of one or more setsof object data may be associated with a level of confidence that theobject data corresponds to an object detected in the respective image.Processing units may determine whether the level of confidenceassociated with a respective set of object data satisfies a level ofconfidence criterion (e.g., exceeds a level of confidence threshold). Inresponse to determining that the level of confidence associated with therespective set of object data satisfies the level of confidencecriterion, processing units may determine the set of object datacorresponds to the detected object.

FIG. 6 illustrates a flow diagram of an example method of training amachine learning model and updating the trained machine learning modelto remove a mask head, according to at least one embodiment. In someembodiments, one or more operations of method 600 may be performed byone or more components or modules of training data generator 131 and/ortraining engine 141, described herein. Processing units performingmethod 600 may identify, at block 610, generate training data for amachine learning model.

At block 612, processing units performing method 600 may generate atraining input including an image depicting an object. At block 614,processing units performing method 600 may generate a target output forthe training input. The target output may include a bounding boxassociated with the depicted object, mask data associated with thedepicted object, and an indication of a class associated with thedepicted object. In some embodiments, processing units may generate thetarget output by providing the image depicting the object as input to anadditional machine learning model that is trained to detect, for a giveninput image, one or more objects depicted in the given input image andto predict, for each of the one or more detected objects, at least maskdata associated with the respective detected object. In someembodiments, the additional machine learning model is further trained topredict a class associated with a respective detected object. Processingunits may obtain the mask data (and in some embodiments the indicationof the class) associated with the object depicted in the image of thetraining input using the additional machine learning model.

In additional or alternative embodiments, processing units may generatethe target output by obtaining ground truth data associated with theimage. As described above the ground truth data can include a boundingbox associated with the depicted object any may be obtained from adatabase that stores an indication of bounding boxes associated withobjects depicted in a set of images. The indication of the boundingboxes may be provided by an accepted bounding box authority or a user ofa platform.

At block 616, processing units performing method 600 may provide thetraining data to train the machine learning model on: (i) a set oftraining inputs including the generated training input and (ii) a set oftarget outputs including the generated target output. At block 618,processing units performing method 600 may identify one or more heads ofthe trained machine learning model that correspond to predicting maskdata for a given input image. At block 620, processing units performingmethod 600 may update the trained machine learning model to remove theone or more identified heads.

In some embodiments, processing units performing method 600, or otherprocessing units, may provide a set of images as input to the updatedtrained machine learning model and obtain one or more outputs of theupdated trained machine learning model. Processing units may determine,from the one or more outputs, object data associated with each of theset of images. The object data may include an indication of a region ofa respective image that includes an object detected in the respectiveimage and a class associated with the detected object. In someembodiments, processing units performing method 600, or other processingunits, may transmit the updated trained machine learning model to atleast one of an edge device or an endpoint device via a network.

Inference and Training Logic

FIG. 7A illustrates inference and/or training logic 715 used to performinferencing and/or training operations associated with one or moreembodiments. Details regarding inference and/or training logic 715 areprovided below in conjunction with FIGS. 7A and/or 7B.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, code and/or data storage 701 to storeforward and/or output weight and/or input/output data, and/or otherparameters to configure neurons or layers of a neural network trainedand/or used for inferencing in aspects of one or more embodiments. In atleast one embodiment, training logic 715 may include, or be coupled tocode and/or data storage 701 to store graph code or other software tocontrol timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs). In at least one embodiment, code, such as graph code, loadsweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which the code corresponds. In atleast one embodiment, code and/or data storage 701 stores weightparameters and/or input/output data of each layer of a neural networktrained or used in conjunction with one or more embodiments duringforward propagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, any portion of code and/or data storage 701 maybe included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 701may be internal or external to one or more processors or other hardwarelogic devices or circuits. In at least one embodiment, code and/or codeand/or data storage 701 may be cache memory, dynamic randomlyaddressable memory (“DRAM”), static randomly addressable memory(“SRAM”), non-volatile memory (e.g., Flash memory), or other storage. Inat least one embodiment, choice of whether code and/or code and/or datastorage 701 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, a code and/or data storage 705 to storebackward and/or output weight and/or input/output data corresponding toneurons or layers of a neural network trained and/or used forinferencing in aspects of one or more embodiments. In at least oneembodiment, code and/or data storage 705 stores weight parameters and/orinput/output data of each layer of a neural network trained or used inconjunction with one or more embodiments during backward propagation ofinput/output data and/or weight parameters during training and/orinferencing using aspects of one or more embodiments. In at least oneembodiment, training logic 715 may include, or be coupled to code and/ordata storage 705 to store graph code or other software to control timingand/or order, in which weight and/or other parameter information is tobe loaded to configure, logic, including integer and/or floating pointunits (collectively, arithmetic logic units (ALUs). In at least oneembodiment, code, such as graph code, loads weight or other parameterinformation into processor ALUs based on an architecture of a neuralnetwork to which the code corresponds. In at least one embodiment, anyportion of code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory. In at least one embodiment, any portion of codeand/or data storage 705 may be internal or external to on one or moreprocessors or other hardware logic devices or circuits. In at least oneembodiment, code and/or data storage 705 may be cache memory, DRAM,SRAM, non-volatile memory (e.g., Flash memory), or other storage. In atleast one embodiment, choice of whether code and/or data storage 705 isinternal or external to a processor, for example, or comprised of DRAM,SRAM, Flash or some other storage type may depend on available storageon-chip versus off-chip, latency requirements of training and/orinferencing functions being performed, batch size of data used ininferencing and/or training of a neural network, or some combination ofthese factors.

In at least one embodiment, code and/or data storage 701 and code and/ordata storage 705 may be separate storage structures. In at least oneembodiment, code and/or data storage 701 and code and/or data storage705 may be same storage structure. In at least one embodiment, codeand/or data storage 701 and code and/or data storage 705 may bepartially same storage structure and partially separate storagestructures. In at least one embodiment, any portion of code and/or datastorage 701 and code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, one or more arithmetic logic unit(s)(“ALU(s)”) 710, including integer and/or floating point units, toperform logical and/or mathematical operations based, at least in parton, or indicated by, training and/or inference code (e.g., graph code),a result of which may produce activations (e.g., output values fromlayers or neurons within a neural network) stored in an activationstorage 720 that are functions of input/output and/or weight parameterdata stored in code and/or data storage 701 and/or code and/or datastorage 705. In at least one embodiment, activations stored inactivation storage 720 are generated according to linear algebraic andor matrix-based mathematics performed by ALU(s) 710 in response toperforming instructions or other code, wherein weight values stored incode and/or data storage 705 and/or code and/or data storage 701 areused as operands along with other values, such as bias values, gradientinformation, momentum values, or other parameters or hyperparameters,any or all of which may be stored in code and/or data storage 705 orcode and/or data storage 701 or another storage on or off-chip.

In at least one embodiment, ALU(s) 710 are included within one or moreprocessors or other hardware logic devices or circuits, whereas inanother embodiment, ALU(s) 710 may be external to a processor or otherhardware logic device or circuit that uses them (e.g., a co-processor).In at least one embodiment, ALUs 710 may be included within aprocessor's execution units or otherwise within a bank of ALUsaccessible by a processor's execution units either within same processoror distributed between different processors of different types (e.g.,central processing units, graphics processing units, fixed functionunits, etc.). In at least one embodiment, code and/or data storage 701,code and/or data storage 705, and activation storage 720 may be on sameprocessor or other hardware logic device or circuit, whereas in anotherembodiment, they may be in different processors or other hardware logicdevices or circuits, or some combination of same and differentprocessors or other hardware logic devices or circuits. In at least oneembodiment, any portion of activation storage 720 may be included withother on-chip or off-chip data storage, including a processor's L1, L2,or L3 cache or system memory. Furthermore, inferencing and/or trainingcode may be stored with other code accessible to a processor or otherhardware logic or circuit and fetched and/or processed using aprocessor's fetch, decode, scheduling, execution, retirement and/orother logical circuits.

In at least one embodiment, activation storage 720 may be cache memory,DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage.In at least one embodiment, activation storage 720 may be completely orpartially within or external to one or more processors or other logicalcircuits. In at least one embodiment, choice of whether activationstorage 720 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors. In at least one embodiment, inferenceand/or training logic 715 illustrated in FIG. 7A may be used inconjunction with an application-specific integrated circuit (“ASIC”),such as Tensorflow® Processing Unit from Google, an inference processingunit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processorfrom Intel Corp. In at least one embodiment, inference and/or traininglogic 715 illustrated in FIG. 7A may be used in conjunction with centralprocessing unit (“CPU”) hardware, graphics processing unit (“GPU”)hardware or other hardware, such as field programmable gate arrays(“FPGAs”).

FIG. 7B illustrates inference and/or training logic 715, according to atleast one or more embodiments. In at least one embodiment, inferenceand/or training logic 715 may include, without limitation, hardwarelogic in which computational resources are dedicated or otherwiseexclusively used in conjunction with weight values or other informationcorresponding to one or more layers of neurons within a neural network.In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7B may be used in conjunction with anapplication-specific integrated circuit (ASIC), such as Tensorflow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7B may be used in conjunction with centralprocessing unit (CPU) hardware, graphics processing unit (GPU) hardwareor other hardware, such as field programmable gate arrays (FPGAs). In atleast one embodiment, inference and/or training logic 715 includes,without limitation, code and/or data storage 701 and code and/or datastorage 705, which may be used to store code (e.g., graph code), weightvalues and/or other information, including bias values, gradientinformation, momentum values, and/or other parameter or hyperparameterinformation. In at least one embodiment illustrated in FIG. 7B, each ofcode and/or data storage 701 and code and/or data storage 705 isassociated with a dedicated computational resource, such ascomputational hardware 702 and computational hardware 706, respectively.In at least one embodiment, each of computational hardware 702 andcomputational hardware 706 comprises one or more ALUs that performmathematical functions, such as linear algebraic functions, only oninformation stored in code and/or data storage 701 and code and/or datastorage 705, respectively, result of which is stored in activationstorage 720.

In at least one embodiment, each of code and/or data storage 701 and 705and corresponding computational hardware 702 and 706, respectively,correspond to different layers of a neural network, such that resultingactivation from one “storage/computational pair 701/702” of code and/ordata storage 701 and computational hardware 702 is provided as an inputto “storage/computational pair 705/706” of code and/or data storage 705and computational hardware 706, in order to mirror conceptualorganization of a neural network. In at least one embodiment, each ofstorage/computational pairs 701/702 and 705/706 may correspond to morethan one neural network layer. In at least one embodiment, additionalstorage/computation pairs (not shown) subsequent to or in parallel withstorage computation pairs 701/702 and 705/706 may be included ininference and/or training logic 715.

Data Center

FIG. 8 illustrates an example data center 800, in which at least oneembodiment may be used. In at least one embodiment, data center 800includes a data center infrastructure layer 810, a framework layer 820,a software layer 830, and an application layer 840.

In at least one embodiment, as shown in FIG. 8 , data centerinfrastructure layer 810 may include a resource orchestrator 812,grouped computing resources 814, and node computing resources (“nodeC.R.s”) 816(1)-816(N), where “N” represents any whole, positive integer.In at least one embodiment, node C.R.s 816(1)-816(N) may include, butare not limited to, any number of central processing units (“CPUs”) orother processors (including accelerators, field programmable gate arrays(FPGAs), graphics processors, etc.), memory devices (e.g., dynamicread-only memory), storage devices (e.g., solid state or disk drives),network input/output (“NW I/O”) devices, network switches, virtualmachines (“VMs”), power modules, and cooling modules, etc. In at leastone embodiment, one or more node C.R.s from among node C.R.s816(1)-816(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in data centers at various geographicallocations (also not shown). Separate groupings of node C.R.s withingrouped computing resources 814 may include grouped compute, network,memory or storage resources that may be configured or allocated tosupport one or more workloads. In at least one embodiment, several nodeC.R.s including CPUs or processors may grouped within one or more racksto provide compute resources to support one or more workloads. In atleast one embodiment, one or more racks may also include any number ofpower modules, cooling modules, and network switches, in anycombination.

In at least one embodiment, resource orchestrator 812 may configure orotherwise control one or more node C.R.s 816(1)-816(N) and/or groupedcomputing resources 814. In at least one embodiment, resourceorchestrator 812 may include a software design infrastructure (“SDI”)management entity for data center 800. In at least one embodiment,resource orchestrator may include hardware, software or some combinationthereof.

In at least one embodiment, as shown in FIG. 8 , framework layer 820includes a job scheduler 822, a configuration manager 824, a resourcemanager 826 and a distributed file system 828. In at least oneembodiment, framework layer 820 may include a framework to supportsoftware 832 of software layer 830 and/or one or more application(s) 842of application layer 840. In at least one embodiment, software 832 orapplication(s) 842 may respectively include web-based service softwareor applications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, framework layer820 may be, but is not limited to, a type of free and open-sourcesoftware web application framework such as Apache Spark™ (hereinafter“Spark”) that may utilize distributed file system 828 for large-scaledata processing (e.g., “big data”). In at least one embodiment, jobscheduler 822 may include a Spark driver to facilitate scheduling ofworkloads supported by various layers of data center 800. In at leastone embodiment, configuration manager 824 may be capable of configuringdifferent layers such as software layer 830 and framework layer 820including Spark and distributed file system 828 for supportinglarge-scale data processing. In at least one embodiment, resourcemanager 826 may be capable of managing clustered or grouped computingresources mapped to or allocated for support of distributed file system828 and job scheduler 822. In at least one embodiment, clustered orgrouped computing resources may include grouped computing resource 814at data center infrastructure layer 810. In at least one embodiment,resource manager 826 may coordinate with resource orchestrator 812 tomanage these mapped or allocated computing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 828 of framework layer 820. The one or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 828 of framework layer 820. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) orother machine learning applications used in conjunction with one or moreembodiments.

In at least one embodiment, any of configuration manager 824, resourcemanager 826, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a data center operator ofdata center 800 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adata center.

In at least one embodiment, data center 800 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments described herein. Forexample, in at least one embodiment, a machine learning model may betrained by calculating weight parameters according to a neural networkarchitecture using software and computing resources described above withrespect to data center 800. In at least one embodiment, trained machinelearning models corresponding to one or more neural networks may be usedto infer or predict information using resources described above withrespect to data center 800 by using weight parameters calculated throughone or more training techniques described herein.

In at least one embodiment, data center may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training and/or inferencing using above-describedresources. Moreover, one or more software and/or hardware resourcesdescribed above may be configured as a service to allow users to trainor performing inferencing of information, such as image recognition,speech recognition, or other artificial intelligence services.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 8 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

Computer Systems

FIG. 9 is a block diagram illustrating an exemplary computer system,which may be a system with interconnected devices and components, asystem-on-a-chip (SOC) or some combination thereof 900 formed with aprocessor that may include execution units to execute an instruction,according to at least one embodiment. In at least one embodiment,computer system 900 may include, without limitation, a component, suchas a processor 902 to employ execution units including logic to performalgorithms for process data, in accordance with present disclosure, suchas in embodiment described herein. In at least one embodiment, computersystem 900 may include processors, such as PENTIUM® Processor family,Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel®Nervana™ microprocessors available from Intel Corporation of SantaClara, Calif., although other systems (including PCs having othermicroprocessors, engineering workstations, set-top boxes and like) mayalso be used. In at least one embodiment, computer system 900 mayexecute a version of WINDOWS' operating system available from MicrosoftCorporation of Redmond, Wash., although other operating systems (UNIXand Linux for example), embedded software, and/or graphical userinterfaces, may also be used.

Embodiments may be used in other devices such as handheld devices andembedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (“PDAs”), and handheld PCs. In at least oneembodiment, embedded applications may include a microcontroller, adigital signal processor (“DSP”), system on a chip, network computers(“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”)switches, or any other system that may perform one or more instructionsin accordance with at least one embodiment.

In at least one embodiment, computer system 900 may include, withoutlimitation, processor 902 that may include, without limitation, one ormore execution units 908 to perform machine learning model trainingand/or inferencing according to techniques described herein. In at leastone embodiment, computer system 900 is a single processor desktop orserver system, but in another embodiment computer system 900 may be amultiprocessor system. In at least one embodiment, processor 902 mayinclude, without limitation, a complex instruction set computer (“CISC”)microprocessor, a reduced instruction set computing (“RISC”)microprocessor, a very long instruction word (“VLIW”) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. In atleast one embodiment, processor 902 may be coupled to a processor bus910 that may transmit data signals between processor 902 and othercomponents in computer system 900.

In at least one embodiment, processor 902 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 904. In atleast one embodiment, processor 902 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 902. Other embodiments may alsoinclude a combination of both internal and external caches depending onparticular implementation and needs. In at least one embodiment,register file 906 may store different types of data in various registersincluding, without limitation, integer registers, floating pointregisters, status registers, and instruction pointer register.

In at least one embodiment, execution unit 908, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 902. In at least one embodiment, processor 902 mayalso include a microcode (“ucode”) read only memory (“ROM”) that storesmicrocode for certain macro instructions. In at least one embodiment,execution unit 908 may include logic to handle a packed instruction set909. In at least one embodiment, by including packed instruction set 909in an instruction set of a general-purpose processor 902, along withassociated circuitry to execute instructions, operations used by manymultimedia applications may be performed using packed data in ageneral-purpose processor 902. In one or more embodiments, manymultimedia applications may be accelerated and executed more efficientlyby using full width of a processor's data bus for performing operationson packed data, which may eliminate need to transfer smaller units ofdata across processor's data bus to perform one or more operations onedata element at a time.

In at least one embodiment, execution unit 908 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system 900may include, without limitation, a memory 920. In at least oneembodiment, memory 920 may be implemented as a Dynamic Random AccessMemory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device,flash memory device, or other memory device. In at least one embodiment,memory 920 may store instruction(s) 919 and/or data 921 represented bydata signals that may be executed by processor 902.

In at least one embodiment, system logic chip may be coupled toprocessor bus 910 and memory 920. In at least one embodiment, systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 916, and processor 902 may communicate with MCH 916 viaprocessor bus 910. In at least one embodiment, MCH 916 may provide ahigh bandwidth memory path 918 to memory 920 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 916 may direct data signals between processor902, memory 920, and other components in computer system 900 and tobridge data signals between processor bus 910, memory 920, and a systemI/O 922. In at least one embodiment, system logic chip may provide agraphics port for coupling to a graphics controller. In at least oneembodiment, MCH 916 may be coupled to memory 920 through a highbandwidth memory path 918 and graphics/video card 912 may be coupled toMCH 916 through an Accelerated Graphics Port (“AGP”) interconnect 914.

In at least one embodiment, computer system 900 may use system I/O 922that is a proprietary hub interface bus to couple MCH 916 to I/Ocontroller hub (“ICH”) 930. In at least one embodiment, ICH 930 mayprovide direct connections to some I/O devices via a local I/O bus. Inat least one embodiment, local I/O bus may include, without limitation,a high-speed I/O bus for connecting peripherals to memory 920, chipset,and processor 902. Examples may include, without limitation, an audiocontroller 929, a firmware hub (“flash BIOS”) 928, a wirelesstransceiver 926, a data storage 924, a legacy I/O controller 923containing user input and keyboard interfaces 925, a serial expansionport 927, such as Universal Serial Bus (“USB”), and a network controller934. Data storage 924 may comprise a hard disk drive, a floppy diskdrive, a CD-ROM device, a flash memory device, or other mass storagedevice.

In at least one embodiment, FIG. 9 illustrates a system, which includesinterconnected hardware devices or “chips,” whereas in otherembodiments, FIG. 9 may illustrate an exemplary System on a Chip(“SoC”). In at least one embodiment, devices may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of computer system 900 are interconnected using computeexpress link (CXL) interconnects.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 9 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 10 is a block diagram illustrating an electronic device 1000 forutilizing a processor 1010, according to at least one embodiment. In atleast one embodiment, electronic device 1000 may be, for example andwithout limitation, a notebook, a tower server, a rack server, a bladeserver, a laptop, a desktop, a tablet, a mobile device, a phone, anembedded computer, or any other suitable electronic device.

In at least one embodiment, system 1000 may include, without limitation,processor 1010 communicatively coupled to any suitable number or kind ofcomponents, peripherals, modules, or devices. In at least oneembodiment, processor 1010 coupled using a bus or interface, such as a1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus,a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”)bus, a Serial Advance Technology Attachment (“SATA”) bus, a UniversalSerial Bus (“USB”) (versions 1, 2, 3), or a Universal AsynchronousReceiver/Transmitter (“UART”) bus. In at least one embodiment, FIG. 10illustrates a system, which includes interconnected hardware devices or“chips,” whereas in other embodiments, FIG. 10 may illustrate anexemplary System on a Chip (“SoC”). In at least one embodiment, devicesillustrated in FIG. 10 may be interconnected with proprietaryinterconnects, standardized interconnects (e.g., PCIe) or somecombination thereof. In at least one embodiment, one or more componentsof FIG. 10 are interconnected using compute express link (CXL)interconnects.

In at least one embodiment, FIG. 10 may include a display 1024, a touchscreen 1025, a touch pad 1030, a Near Field Communications unit (“NFC”)1045, a sensor hub 1040, a thermal sensor 1046, an Express Chipset(“EC”) 1035, a Trusted Platform Module (“TPM”) 1038, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 such as a SolidState Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local areanetwork unit (“WLAN”) 1050, a Bluetooth unit 1052, a Wireless Wide AreaNetwork unit (“WWAN”) 1056, a Global Positioning System (GPS) 1055, acamera (“USB 3.0 camera”) 1054 such as a USB 3.0 camera, and/or a LowPower Double Data Rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implementedin, for example, LPDDR3 standard. These components may each beimplemented in any suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 1010 through components discussed above. In atleast one embodiment, an accelerometer 1041, Ambient Light Sensor(“ALS”) 1042, compass 1043, and a gyroscope 1044 may be communicativelycoupled to sensor hub 1040. In at least one embodiment, thermal sensor1039, a fan 1037, a keyboard 1046, and a touch pad 1030 may becommunicatively coupled to EC 1035. In at least one embodiment, speaker1063, headphones 1064, and microphone (“mic”) 1065 may becommunicatively coupled to an audio unit (“audio codec and class d amp”)1062, which may in turn be communicatively coupled to DSP 1060. In atleast one embodiment, audio unit 1064 may include, for example andwithout limitation, an audio coder/decoder (“codec”) and a class Damplifier. In at least one embodiment, SIM card (“SIM”) 1057 may becommunicatively coupled to WWAN unit 1056. In at least one embodiment,components such as WLAN unit 1050 and Bluetooth unit 1052, as well asWWAN unit 1056 may be implemented in a Next Generation Form Factor(“NGFF”).

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 10 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 11 is a block diagram of a processing system, according to at leastone embodiment. In at least one embodiment, system 1100 includes one ormore processors 1102 and one or more graphics processors 1108, and maybe a single processor desktop system, a multiprocessor workstationsystem, or a server system having a large number of processors 1102 orprocessor cores 1107. In at least one embodiment, system 1100 is aprocessing platform incorporated within a system-on-a-chip (SoC)integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 1100 can include, or be incorporatedwithin a server-based gaming platform, a game console, including a gameand media console, a mobile gaming console, a handheld game console, oran online game console. In at least one embodiment, system 1100 is amobile phone, smart phone, tablet computing device or mobile Internetdevice. In at least one embodiment, processing system 1100 can alsoinclude, couple with, or be integrated within a wearable device, such asa smart watch wearable device, smart eyewear device, augmented realitydevice, or virtual reality device. In at least one embodiment,processing system 1100 is a television or set top box device having oneor more processors 1102 and a graphical interface generated by one ormore graphics processors 1108.

In at least one embodiment, one or more processors 1102 each include oneor more processor cores 1107 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 1107 is configuredto process a specific instruction set 1109. In at least one embodiment,instruction set 1109 may facilitate Complex Instruction Set Computing(CISC), Reduced Instruction Set Computing (RISC), or computing via aVery Long Instruction Word (VLIW). In at least one embodiment, processorcores 1107 may each process a different instruction set 1109, which mayinclude instructions to facilitate emulation of other instruction sets.In at least one embodiment, processor core 1107 may also include otherprocessing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor 1102 includes cache memory 1104.In at least one embodiment, processor 1102 can have a single internalcache or multiple levels of internal cache. In at least one embodiment,cache memory is shared among various components of processor 1102. In atleast one embodiment, processor 1102 also uses an external cache (e.g.,a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which maybe shared among processor cores 1107 using known cache coherencytechniques. In at least one embodiment, register file 1106 isadditionally included in processor 1102 which may include differenttypes of registers for storing different types of data (e.g., integerregisters, floating point registers, status registers, and aninstruction pointer register). In at least one embodiment, register file1106 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 1102 are coupledwith one or more interface bus(es) 1110 to transmit communicationsignals such as address, data, or control signals between processor 1102and other components in system 1100. In at least one embodiment,interface bus 1110, in one embodiment, can be a processor bus, such as aversion of a Direct Media Interface (DMI) bus. In at least oneembodiment, interface 1110 is not limited to a DMI bus, and may includeone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress), memory busses, or other types of interface busses. In at leastone embodiment processor(s) 1102 include an integrated memory controller1116 and a platform controller hub 1130. In at least one embodiment,memory controller 1116 facilitates communication between a memory deviceand other components of system 1100, while platform controller hub (PCH)1130 provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device 1120 can be a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as process memory. Inat least one embodiment memory device 1120 can operate as system memoryfor system 1100, to store data 1122 and instructions 1121 for use whenone or more processors 1102 executes an application or process. In atleast one embodiment, memory controller 1116 also couples with anoptional external graphics processor 1112, which may communicate withone or more graphics processors 1108 in processors 1102 to performgraphics and media operations. In at least one embodiment, a displaydevice 1111 can connect to processor(s) 1102. In at least one embodimentdisplay device 1111 can include one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 1111 caninclude a head mounted display (HMD) such as a stereoscopic displaydevice for use in virtual reality (VR) applications or augmented reality(AR) applications.

In at least one embodiment, platform controller hub 1130 enablesperipherals to connect to memory device 1120 and processor 1102 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 1146, a network controller1134, a firmware interface 1128, a wireless transceiver 1126, touchsensors 1125, a data storage device 1124 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 1124 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). In at least one embodiment, touch sensors 1125 can includetouch screen sensors, pressure sensors, or fingerprint sensors. In atleast one embodiment, wireless transceiver 1126 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at leastone embodiment, firmware interface 1128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). In at least one embodiment, network controller 1134can enable a network connection to a wired network. In at least oneembodiment, a high-performance network controller (not shown) coupleswith interface bus 1110. In at least one embodiment, audio controller1146 is a multi-channel high definition audio controller. In at leastone embodiment, system 1100 includes an optional legacy I/O controller1140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices tosystem. In at least one embodiment, platform controller hub 1130 canalso connect to one or more Universal Serial Bus (USB) controllers 1142connect input devices, such as keyboard and mouse 1143 combinations, acamera 1144, or other USB input devices.

In at least one embodiment, an instance of memory controller 1116 andplatform controller hub 1130 may be integrated into a discreet externalgraphics processor, such as external graphics processor 1112. In atleast one embodiment, platform controller hub 1130 and/or memorycontroller 1116 may be external to one or more processor(s) 1102. Forexample, in at least one embodiment, system 1100 can include an externalmemory controller 1116 and platform controller hub 1130, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 1102.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into graphics processor 1500. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in a graphics processor. Moreover, inat least one embodiment, inferencing and/or training operationsdescribed herein may be done using logic other than logic illustrated inFIG. 7A or 7B. In at least one embodiment, weight parameters may bestored in on-chip or off-chip memory and/or registers (shown or notshown) that configure ALUs of a graphics processor to perform one ormore machine learning algorithms, neural network architectures, usecases, or training techniques described herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 12 is a block diagram of a processor 1200 having one or moreprocessor cores 1202A-1202N, an integrated memory controller 1214, andan integrated graphics processor 1208, according to at least oneembodiment. In at least one embodiment, processor 1200 can includeadditional cores up to and including additional core 1202N representedby dashed lined boxes. In at least one embodiment, each of processorcores 1202A-1202N includes one or more internal cache units 1204A-1204N.In at least one embodiment, each processor core also has access to oneor more shared cached units 1206.

In at least one embodiment, internal cache units 1204A-1204N and sharedcache units 1206 represent a cache memory hierarchy within processor1200. In at least one embodiment, cache memory units 1204A-1204N mayinclude at least one level of instruction and data cache within eachprocessor core and one or more levels of shared mid-level cache, such asa Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache,where a highest level of cache before external memory is classified asan LLC. In at least one embodiment, cache coherency logic maintainscoherency between various cache units 1206 and 1204A-1204N.

In at least one embodiment, processor 1200 may also include a set of oneor more bus controller units 1216 and a system agent core 1210. In atleast one embodiment, one or more bus controller units 1216 manage a setof peripheral buses, such as one or more PCI or PCI express busses. Inat least one embodiment, system agent core 1210 provides managementfunctionality for various processor components. In at least oneembodiment, system agent core 1210 includes one or more integratedmemory controllers 1214 to manage access to various external memorydevices (not shown).

In at least one embodiment, one or more of processor cores 1202A-1202Ninclude support for simultaneous multi-threading. In at least oneembodiment, system agent core 1210 includes components for coordinatingand operating cores 1202A-1202N during multi-threaded processing. In atleast one embodiment, system agent core 1210 may additionally include apower control unit (PCU), which includes logic and components toregulate one or more power states of processor cores 1202A-1202N andgraphics processor 1208.

In at least one embodiment, processor 1200 additionally includesgraphics processor 1208 to execute graphics processing operations. In atleast one embodiment, graphics processor 1208 couples with shared cacheunits 1206, and system agent core 1210, including one or more integratedmemory controllers 1214. In at least one embodiment, system agent core1210 also includes a display controller 1211 to drive graphics processoroutput to one or more coupled displays. In at least one embodiment,display controller 1211 may also be a separate module coupled withgraphics processor 1208 via at least one interconnect, or may beintegrated within graphics processor 1208.

In at least one embodiment, a ring based interconnect unit 1212 is usedto couple internal components of processor 1200. In at least oneembodiment, an alternative interconnect unit may be used, such as apoint-to-point interconnect, a switched interconnect, or othertechniques. In at least one embodiment, graphics processor 1208 coupleswith ring interconnect 1212 via an I/O link 1213.

In at least one embodiment, I/O link 1213 represents at least one ofmultiple varieties of I/O interconnects, including an on package I/Ointerconnect which facilitates communication between various processorcomponents and a high-performance embedded memory module 1218, such asan eDRAM module. In at least one embodiment, each of processor cores1202A-1202N and graphics processor 1208 use embedded memory modules 1218as a shared Last Level Cache.

In at least one embodiment, processor cores 1202A-1202N are homogenouscores executing a common instruction set architecture. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofinstruction set architecture (ISA), where one or more of processor cores1202A-1202N execute a common instruction set, while one or more othercores of processor cores 1202A-1202N executes a subset of a commoninstruction set or a different instruction set. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. In at least one embodiment, processor 1200 can beimplemented on one or more chips or as a SoC integrated circuit.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into processor 1200. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in graphics processor 1512, graphicscore(s) 1202A-1202N, or other components in FIG. 12 . Moreover, in atleast one embodiment, inferencing and/or training operations describedherein may be done using logic other than logic illustrated in FIG. 7Aor 7B. In at least one embodiment, weight parameters may be stored inon-chip or off-chip memory and/or registers (shown or not shown) thatconfigure ALUs of graphics processor 1200 to perform one or more machinelearning algorithms, neural network architectures, use cases, ortraining techniques described herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

Virtualized Computing Platform

FIG. 13 is an example data flow diagram for a process 1300 of generatingand deploying an image processing and inferencing pipeline, inaccordance with at least one embodiment. In at least one embodiment,process 1300 may be deployed for use with imaging devices, processingdevices, and/or other device types at one or more facilities 1302.Process 1300 may be executed within a training system 1304 and/or adeployment system 1306. In at least one embodiment, training system 1304may be used to perform training, deployment, and implementation ofmachine learning models (e.g., neural networks, object detectionalgorithms, computer vision algorithms, etc.) for use in deploymentsystem 1306. In at least one embodiment, deployment system 1306 may beconfigured to offload processing and compute resources among adistributed computing environment to reduce infrastructure requirementsat facility 1302. In at least one embodiment, one or more applicationsin a pipeline may use or call upon services (e.g., inference,visualization, compute, AI, etc.) of deployment system 1306 duringexecution of applications.

In at least one embodiment, some of applications used in advancedprocessing and inferencing pipelines may use machine learning models orother AI to perform one or more processing steps. In at least oneembodiment, machine learning models may be trained at facility 1302using data 1308 (such as imaging data) generated at facility 1302 (andstored on one or more picture archiving and communication system (PACS)servers at facility 1302), may be trained using imaging or sequencingdata 1308 from another facility(ies), or a combination thereof. In atleast one embodiment, training system 1304 may be used to provideapplications, services, and/or other resources for generating working,deployable machine learning models for deployment system 1306.

In at least one embodiment, model registry 1324 may be backed by objectstorage that may support versioning and object metadata. In at least oneembodiment, object storage may be accessible through, for example, acloud storage (e.g., cloud 1426 of FIG. 14 ) compatible applicationprogramming interface (API) from within a cloud platform. In at leastone embodiment, machine learning models within model registry 1324 mayuploaded, listed, modified, or deleted by developers or partners of asystem interacting with an API. In at least one embodiment, an API mayprovide access to methods that allow users with appropriate credentialsto associate models with applications, such that models may be executedas part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline 1404 (FIG. 14 ) mayinclude a scenario where facility 1302 is training their own machinelearning model, or has an existing machine learning model that needs tobe optimized or updated. In at least one embodiment, imaging data 1308generated by imaging device(s), sequencing devices, and/or other devicetypes may be received. In at least one embodiment, once imaging data1308 is received, AI-assisted annotation 1310 may be used to aid ingenerating annotations corresponding to imaging data 1308 to be used asground truth data for a machine learning model. In at least oneembodiment, AI-assisted annotation 1310 may include one or more machinelearning models (e.g., convolutional neural networks (CNNs)) that may betrained to generate annotations corresponding to certain types ofimaging data 1308 (e.g., from certain devices). In at least oneembodiment, AI-assisted annotations 1310 may then be used directly, ormay be adjusted or fine-tuned using an annotation tool to generateground truth data. In at least one embodiment, AI-assisted annotations1310, labeled clinic data 1312, or a combination thereof may be used asground truth data for training a machine learning model. In at least oneembodiment, a trained machine learning model may be referred to asoutput model 1316, and may be used by deployment system 1306, asdescribed herein.

In at least one embodiment, training pipeline 1404 (FIG. 14 ) mayinclude a scenario where facility 1302 needs a machine learning modelfor use in performing one or more processing tasks for one or moreapplications in deployment system 1306, but facility 1302 may notcurrently have such a machine learning model (or may not have a modelthat is optimized, efficient, or effective for such purposes). In atleast one embodiment, an existing machine learning model may be selectedfrom a model registry 1324. In at least one embodiment, model registry1324 may include machine learning models trained to perform a variety ofdifferent inference tasks on imaging data. In at least one embodiment,machine learning models in model registry 1324 may have been trained onimaging data from different facilities than facility 1302 (e.g.,facilities remotely located). In at least one embodiment, machinelearning models may have been trained on imaging data from one location,two locations, or any number of locations. In at least one embodiment,when being trained on imaging data from a specific location, trainingmay take place at that location, or at least in a manner that protectsconfidentiality of imaging data or restricts imaging data from beingtransferred off-premises. In at least one embodiment, once a model istrained—or partially trained—at one location, a machine learning modelmay be added to model registry 1324. In at least one embodiment, amachine learning model may then be retrained, or updated, at any numberof other facilities, and a retrained or updated model may be madeavailable in model registry 1324. In at least one embodiment, a machinelearning model may then be selected from model registry 1324—andreferred to as output model 1316—and may be used in deployment system1306 to perform one or more processing tasks for one or moreapplications of a deployment system.

In at least one embodiment, training pipeline 1404 (FIG. 14 ), ascenario may include facility 1302 requiring a machine learning modelfor use in performing one or more processing tasks for one or moreapplications in deployment system 1306, but facility 1302 may notcurrently have such a machine learning model (or may not have a modelthat is optimized, efficient, or effective for such purposes). In atleast one embodiment, a machine learning model selected from modelregistry 1324 may not be fine-tuned or optimized for imaging data 1308generated at facility 1302 because of differences in populations,robustness of training data used to train a machine learning model,diversity in anomalies of training data, and/or other issues withtraining data. In at least one embodiment, AI-assisted annotation 1310may be used to aid in generating annotations corresponding to imagingdata 1308 to be used as ground truth data for retraining or updating amachine learning model. In at least one embodiment, labeled data 1312may be used as ground truth data for training a machine learning model.In at least one embodiment, retraining or updating a machine learningmodel may be referred to as model training 1314. In at least oneembodiment, model training 1314—e.g., AI-assisted annotations 1310,labeled clinic data 1312, or a combination thereof—may be used as groundtruth data for retraining or updating a machine learning model. In atleast one embodiment, a trained machine learning model may be referredto as output model 1316, and may be used by deployment system 1306, asdescribed herein.

In at least one embodiment, deployment system 1306 may include software1318, services 1320, hardware 1322, and/or other components, features,and functionality. In at least one embodiment, deployment system 1306may include a software “stack,” such that software 1318 may be built ontop of services 1320 and may use services 1320 to perform some or all ofprocessing tasks, and services 1320 and software 1318 may be built ontop of hardware 1322 and use hardware 1322 to execute processing,storage, and/or other compute tasks of deployment system 1306. In atleast one embodiment, software 1318 may include any number of differentcontainers, where each container may execute an instantiation of anapplication. In at least one embodiment, each application may performone or more processing tasks in an advanced processing and inferencingpipeline (e.g., inferencing, object detection, feature detection,segmentation, image enhancement, calibration, etc.). In at least oneembodiment, an advanced processing and inferencing pipeline may bedefined based on selections of different containers that are desired orrequired for processing imaging data 1308, in addition to containersthat receive and configure imaging data for use by each container and/orfor use by facility 1302 after processing through a pipeline (e.g., toconvert outputs back to a usable data type). In at least one embodiment,a combination of containers within software 1318 (e.g., that make up apipeline) may be referred to as a virtual instrument (as described inmore detail herein), and a virtual instrument may leverage services 1320and hardware 1322 to execute some or all processing tasks ofapplications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive inputdata (e.g., imaging data 1308) in a specific format in response to aninference request (e.g., a request from a user of deployment system1306). In at least one embodiment, input data may be representative ofone or more images, video, and/or other data representations generatedby one or more imaging devices. In at least one embodiment, data mayundergo pre-processing as part of data processing pipeline to preparedata for processing by one or more applications. In at least oneembodiment, post-processing may be performed on an output of one or moreinferencing tasks or other processing tasks of a pipeline to prepare anoutput data for a next application and/or to prepare output data fortransmission and/or use by a user (e.g., as a response to an inferencerequest). In at least one embodiment, inferencing tasks may be performedby one or more machine learning models, such as trained or deployedneural networks, which may include output models 1316 of training system1304.

In at least one embodiment, tasks of data processing pipeline may beencapsulated in a container(s) that each represents a discrete, fullyfunctional instantiation of an application and virtualized computingenvironment that is able to reference machine learning models. In atleast one embodiment, containers or applications may be published into aprivate (e.g., limited access) area of a container registry (describedin more detail herein), and trained or deployed models may be stored inmodel registry 1324 and associated with one or more applications. In atleast one embodiment, images of applications (e.g., container images)may be available in a container registry, and once selected by a userfrom a container registry for deployment in a pipeline, an image may beused to generate a container for an instantiation of an application foruse by a user's system.

In at least one embodiment, developers (e.g., software developers,clinicians, doctors, etc.) may develop, publish, and store applications(e.g., as containers) for performing image processing and/or inferencingon supplied data. In at least one embodiment, development, publishing,and/or storing may be performed using a software development kit (SDK)associated with a system (e.g., to ensure that an application and/orcontainer developed is compliant with or compatible with a system). Inat least one embodiment, an application that is developed may be testedlocally (e.g., at a first facility, on data from a first facility) withan SDK which may support at least some of services 1320 as a system(e.g., system 1400 of FIG. 14 ). In at least one embodiment, becauseDICOM objects may contain anywhere from one to hundreds of images orother data types, and due to a variation in data, a developer may beresponsible for managing (e.g., setting constructs for, buildingpre-processing into an application, etc.) extraction and preparation ofincoming data. In at least one embodiment, once validated by system 1400(e.g., for accuracy), an application may be available in a containerregistry for selection and/or implementation by a user to perform one ormore processing tasks with respect to data at a facility (e.g., a secondfacility) of a user.

In at least one embodiment, developers may then share applications orcontainers through a network for access and use by users of a system(e.g., system 1400 of FIG. 14 ). In at least one embodiment, completedand validated applications or containers may be stored in a containerregistry and associated machine learning models may be stored in modelregistry 1324. In at least one embodiment, a requesting entity—whoprovides an inference or image processing request—may browse a containerregistry and/or model registry 1324 for an application, container,dataset, machine learning model, etc., select a desired combination ofelements for inclusion in data processing pipeline, and submit animaging processing request. In at least one embodiment, a request mayinclude input data (and associated patient data, in some examples) thatis necessary to perform a request, and/or may include a selection ofapplication(s) and/or machine learning models to be executed inprocessing a request. In at least one embodiment, a request may then bepassed to one or more components of deployment system 1306 (e.g., acloud) to perform processing of data processing pipeline. In at leastone embodiment, processing by deployment system 1306 may includereferencing selected elements (e.g., applications, containers, models,etc.) from a container registry and/or model registry 1324. In at leastone embodiment, once results are generated by a pipeline, results may bereturned to a user for reference (e.g., for viewing in a viewingapplication suite executing on a local, on-premises workstation orterminal).

In at least one embodiment, to aid in processing or execution ofapplications or containers in pipelines, services 1320 may be leveraged.In at least one embodiment, services 1320 may include compute services,artificial intelligence (AI) services, visualization services, and/orother service types. In at least one embodiment, services 1320 mayprovide functionality that is common to one or more applications insoftware 1318, so functionality may be abstracted to a service that maybe called upon or leveraged by applications. In at least one embodiment,functionality provided by services 1320 may run dynamically and moreefficiently, while also scaling well by allowing applications to processdata in parallel (e.g., using a parallel computing platform 1430 (FIG.14 )). In at least one embodiment, rather than each application thatshares a same functionality offered by a service 1320 being required tohave a respective instance of service 1320, service 1320 may be sharedbetween and among various applications. In at least one embodiment,services may include an inference server or engine that may be used forexecuting detection or segmentation tasks, as non-limiting examples. Inat least one embodiment, a model training service may be included thatmay provide machine learning model training and/or retrainingcapabilities. In at least one embodiment, a data augmentation servicemay further be included that may provide GPU accelerated data (e.g.,DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing,scaling, and/or other augmentation. In at least one embodiment, avisualization service may be used that may add image renderingeffects—such as ray-tracing, rasterization, denoising, sharpening,etc.—to add realism to two-dimensional (2D) and/or three-dimensional(3D) models. In at least one embodiment, virtual instrument services maybe included that provide for beam-forming, segmentation, inferencing,imaging, and/or support for other applications within pipelines ofvirtual instruments.

In at least one embodiment, where a service 1320 includes an AI service(e.g., an inference service), one or more machine learning models may beexecuted by calling upon (e.g., as an API call) an inference service(e.g., an inference server) to execute machine learning model(s), orprocessing thereof, as part of application execution. In at least oneembodiment, where another application includes one or more machinelearning models for segmentation tasks, an application may call upon aninference service to execute machine learning models for performing oneor more of processing operations associated with segmentation tasks. Inat least one embodiment, software 1318 implementing advanced processingand inferencing pipeline that includes segmentation application andanomaly detection application may be streamlined because eachapplication may call upon a same inference service to perform one ormore inferencing tasks.

In at least one embodiment, hardware 1322 may include GPUs, CPUs,graphics cards, an AI/deep learning system (e.g., an AI supercomputer,such as NVIDIA's DGX), a cloud platform, or a combination thereof. In atleast one embodiment, different types of hardware 1322 may be used toprovide efficient, purpose-built support for software 1318 and services1320 in deployment system 1306. In at least one embodiment, use of GPUprocessing may be implemented for processing locally (e.g., at facility1302), within an AI/deep learning system, in a cloud system, and/or inother processing components of deployment system 1306 to improveefficiency, accuracy, and efficacy of image processing and generation.In at least one embodiment, software 1318 and/or services 1320 may beoptimized for GPU processing with respect to deep learning, machinelearning, and/or high-performance computing, as non-limiting examples.In at least one embodiment, at least some of computing environment ofdeployment system 1306 and/or training system 1304 may be executed in adatacenter one or more supercomputers or high performance computingsystems, with GPU optimized software (e.g., hardware and softwarecombination of NVIDIA's DGX System). In at least one embodiment,hardware 1322 may include any number of GPUs that may be called upon toperform processing of data in parallel, as described herein. In at leastone embodiment, cloud platform may further include GPU processing forGPU-optimized execution of deep learning tasks, machine learning tasks,or other computing tasks. In at least one embodiment, cloud platform(e.g., NVIDIA's NGC) may be executed using an AI/deep learningsupercomputer(s) and/or GPU-optimized software (e.g., as provided onNVIDIA's DGX Systems) as a hardware abstraction and scaling platform. Inat least one embodiment, cloud platform may integrate an applicationcontainer clustering system or orchestration system (e.g., KUBERNETES)on multiple GPUs to enable seamless scaling and load balancing.

FIG. 14 is a system diagram for an example system 1400 for generatingand deploying an imaging deployment pipeline, in accordance with atleast one embodiment. In at least one embodiment, system 1400 may beused to implement process 1300 of FIG. 13 and/or other processesincluding advanced processing and inferencing pipelines. In at least oneembodiment, system 1400 may include training system 1304 and deploymentsystem 1306. In at least one embodiment, training system 1304 anddeployment system 1306 may be implemented using software 1318, services1320, and/or hardware 1322, as described herein.

In at least one embodiment, system 1400 (e.g., training system 1304and/or deployment system 1306) may implemented in a cloud computingenvironment (e.g., using cloud 1426). In at least one embodiment, system1400 may be implemented locally with respect to a healthcare servicesfacility, or as a combination of both cloud and local computingresources. In at least one embodiment, access to APIs in cloud 1426 maybe restricted to authorized users through enacted security measures orprotocols. In at least one embodiment, a security protocol may includeweb tokens that may be signed by an authentication (e.g., AuthN, AuthZ,Gluecon, etc.) service and may carry appropriate authorization. In atleast one embodiment, APIs of virtual instruments (described herein), orother instantiations of system 1400, may be restricted to a set ofpublic IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system 1400 maycommunicate between and among one another using any of a variety ofdifferent network types, including but not limited to local areanetworks (LANs) and/or wide area networks (WANs) via wired and/orwireless communication protocols. In at least one embodiment,communication between facilities and components of system 1400 (e.g.,for transmitting inference requests, for receiving results of inferencerequests, etc.) may be communicated over data bus(ses), wireless dataprotocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system 1304 may execute trainingpipelines 1404, similar to those described herein with respect to FIG.13 . In at least one embodiment, where one or more machine learningmodels are to be used in deployment pipelines 1410 by deployment system1306, training pipelines 1404 may be used to train or retrain one ormore (e.g. pre-trained) models, and/or implement one or more ofpre-trained models 1406 (e.g., without a need for retraining orupdating). In at least one embodiment, as a result of training pipelines1404, output model(s) 1316 may be generated. In at least one embodiment,training pipelines 1404 may include any number of processing steps, suchas but not limited to imaging data (or other input data) conversion oradaption In at least one embodiment, for different machine learningmodels used by deployment system 1306, different training pipelines 1404may be used. In at least one embodiment, training pipeline 1404 similarto a first example described with respect to FIG. 13 may be used for afirst machine learning model, training pipeline 1404 similar to a secondexample described with respect to FIG. 13 may be used for a secondmachine learning model, and training pipeline 1404 similar to a thirdexample described with respect to FIG. 13 may be used for a thirdmachine learning model. In at least one embodiment, any combination oftasks within training system 1304 may be used depending on what isrequired for each respective machine learning model. In at least oneembodiment, one or more of machine learning models may already betrained and ready for deployment so machine learning models may notundergo any processing by training system 1304, and may be implementedby deployment system 1306.

In at least one embodiment, output model(s) 1316 and/or pre-trainedmodel(s) 1406 may include any types of machine learning models dependingon implementation or embodiment. In at least one embodiment, and withoutlimitation, machine learning models used by system 1400 may includemachine learning model(s) using linear regression, logistic regression,decision trees, support vector machines (SVM), Naïve Bayes, k-nearestneighbor (Knn), K means clustering, random forest, dimensionalityreduction algorithms, gradient boosting algorithms, neural networks(e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/ShortTerm Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In at least one embodiment, training pipelines 1404 may includeAI-assisted annotation, as described in more detail herein with respectto at least FIG. 15B. In at least one embodiment, labeled data 1312(e.g., traditional annotation) may be generated by any number oftechniques. In at least one embodiment, labels or other annotations maybe generated within a drawing program (e.g., an annotation program), acomputer aided design (CAD) program, a labeling program, another type ofprogram suitable for generating annotations or labels for ground truth,and/or may be hand drawn, in some examples. In at least one embodiment,ground truth data may be synthetically produced (e.g., generated fromcomputer models or renderings), real produced (e.g., designed andproduced from real-world data), machine-automated (e.g., using featureanalysis and learning to extract features from data and then generatelabels), human annotated (e.g., labeler, or annotation expert, defineslocation of labels), and/or a combination thereof. In at least oneembodiment, for each instance of imaging data 1308 (or other data typeused by machine learning models), there may be corresponding groundtruth data generated by training system 1304. In at least oneembodiment, AI-assisted annotation may be performed as part ofdeployment pipelines 1410; either in addition to, or in lieu ofAI-assisted annotation included in training pipelines 1404. In at leastone embodiment, system 1400 may include a multi-layer platform that mayinclude a software layer (e.g., software 1318) of diagnosticapplications (or other application types) that may perform one or moremedical imaging and diagnostic functions. In at least one embodiment,system 1400 may be communicatively coupled to (e.g., via encryptedlinks) PACS server networks of one or more facilities. In at least oneembodiment, system 1400 may be configured to access and referenced datafrom PACS servers to perform operations, such as training machinelearning models, deploying machine learning models, image processing,inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as asecure, encrypted, and/or authenticated API through which applicationsor containers may be invoked (e.g., called) from an externalenvironment(s) (e.g., facility 1302). In at least one embodiment,applications may then call or execute one or more services 1320 forperforming compute, AI, or visualization tasks associated withrespective applications, and software 1318 and/or services 1320 mayleverage hardware 1322 to perform processing tasks in an effective andefficient manner.

In at least one embodiment, deployment system 1306 may executedeployment pipelines 1410. In at least one embodiment, deploymentpipelines 1410 may include any number of applications that may besequentially, non-sequentially, or otherwise applied to imaging data(and/or other data types) generated by imaging devices, sequencingdevices, genomics devices, etc.—including AI-assisted annotation, asdescribed above. In at least one embodiment, as described herein, adeployment pipeline 1410 for an individual device may be referred to asa virtual instrument for a device (e.g., a virtual ultrasoundinstrument, a virtual CT scan instrument, a virtual sequencinginstrument, etc.). In at least one embodiment, for a single device,there may be more than one deployment pipeline 1410 depending oninformation desired from data generated by a device. In at least oneembodiment, where detections of anomalies are desired from an MRImachine, there may be a first deployment pipeline 1410, and where imageenhancement is desired from output of an MRI machine, there may be asecond deployment pipeline 1410.

In at least one embodiment, an image generation application may includea processing task that includes use of a machine learning model. In atleast one embodiment, a user may desire to use their own machinelearning model, or to select a machine learning model from modelregistry 1324. In at least one embodiment, a user may implement theirown machine learning model or select a machine learning model forinclusion in an application for performing a processing task. In atleast one embodiment, applications may be selectable and customizable,and by defining constructs of applications, deployment andimplementation of applications for a particular user are presented as amore seamless user experience. In at least one embodiment, by leveragingother features of system 1400—such as services 1320 and hardware1322—deployment pipelines 1410 may be even more user friendly, providefor easier integration, and produce more accurate, efficient, and timelyresults.

In at least one embodiment, deployment system 1306 may include a userinterface 1414 (e.g., a graphical user interface, a web interface, etc.)that may be used to select applications for inclusion in deploymentpipeline(s) 1410, arrange applications, modify or change applications orparameters or constructs thereof, use and interact with deploymentpipeline(s) 1410 during set-up and/or deployment, and/or to otherwiseinteract with deployment system 1306. In at least one embodiment,although not illustrated with respect to training system 1304, userinterface 1414 (or a different user interface) may be used for selectingmodels for use in deployment system 1306, for selecting models fortraining, or retraining, in training system 1304, and/or for otherwiseinteracting with training system 1304.

In at least one embodiment, pipeline manager 1412 may be used, inaddition to an application orchestration system 1428, to manageinteraction between applications or containers of deployment pipeline(s)1410 and services 1320 and/or hardware 1322. In at least one embodiment,pipeline manager 1412 may be configured to facilitate interactions fromapplication to application, from application to service 1320, and/orfrom application or service to hardware 1322. In at least oneembodiment, although illustrated as included in software 1318, this isnot intended to be limiting, and in some examples (e.g., as illustratedin FIG. 12 cc) pipeline manager 1412 may be included in services 1320.In at least one embodiment, application orchestration system 1428 (e.g.,Kubernetes, DOCKER, etc.) may include a container orchestration systemthat may group applications into containers as logical units forcoordination, management, scaling, and deployment. In at least oneembodiment, by associating applications from deployment pipeline(s) 1410(e.g., a reconstruction application, a segmentation application, etc.)with individual containers, each application may execute in aself-contained environment (e.g., at a kernel level) to increase speedand efficiency.

In at least one embodiment, each application and/or container (or imagethereof) may be individually developed, modified, and deployed (e.g., afirst user or developer may develop, modify, and deploy a firstapplication and a second user or developer may develop, modify, anddeploy a second application separate from a first user or developer),which may allow for focus on, and attention to, a task of a singleapplication and/or container(s) without being hindered by tasks ofanother application(s) or container(s). In at least one embodiment,communication, and cooperation between different containers orapplications may be aided by pipeline manager 1412 and applicationorchestration system 1428. In at least one embodiment, so long as anexpected input and/or output of each container or application is knownby a system (e.g., based on constructs of applications or containers),application orchestration system 1428 and/or pipeline manager 1412 mayfacilitate communication among and between, and sharing of resourcesamong and between, each of applications or containers. In at least oneembodiment, because one or more of applications or containers indeployment pipeline(s) 1410 may share same services and resources,application orchestration system 1428 may orchestrate, load balance, anddetermine sharing of services or resources between and among variousapplications or containers. In at least one embodiment, a scheduler maybe used to track resource requirements of applications or containers,current usage or planned usage of these resources, and resourceavailability. In at least one embodiment, a scheduler may thus allocateresources to different applications and distribute resources between andamong applications in view of requirements and availability of a system.In some examples, a scheduler (and/or other component of applicationorchestration system 1428) may determine resource availability anddistribution based on constraints imposed on a system (e.g., userconstraints), such as quality of service (QoS), urgency of need for dataoutputs (e.g., to determine whether to execute real-time processing ordelayed processing), etc.

In at least one embodiment, services 1320 leveraged by and shared byapplications or containers in deployment system 1306 may include computeservices 1416, AI services 1418, visualization services 1420, and/orother service types. In at least one embodiment, applications may call(e.g., execute) one or more of services 1320 to perform processingoperations for an application. In at least one embodiment, computeservices 1416 may be leveraged by applications to performsuper-computing or other high-performance computing (HPC) tasks. In atleast one embodiment, compute service(s) 1416 may be leveraged toperform parallel processing (e.g., using a parallel computing platform1430) for processing data through one or more of applications and/or oneor more tasks of a single application, substantially simultaneously. Inat least one embodiment, parallel computing platform 1430 (e.g.,NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU)(e.g., GPUs 1422). In at least one embodiment, a software layer ofparallel computing platform 1430 may provide access to virtualinstruction sets and parallel computational elements of GPUs, forexecution of compute kernels. In at least one embodiment, parallelcomputing platform 1430 may include memory and, in some embodiments, amemory may be shared between and among multiple containers, and/orbetween and among different processing tasks within a single container.In at least one embodiment, inter-process communication (IPC) calls maybe generated for multiple containers and/or for multiple processeswithin a container to use same data from a shared segment of memory ofparallel computing platform 1430 (e.g., where multiple different stagesof an application or multiple applications are processing sameinformation). In at least one embodiment, rather than making a copy ofdata and moving data to different locations in memory (e.g., aread/write operation), same data in same location of a memory may beused for any number of processing tasks (e.g., at a same time, atdifferent times, etc.). In at least one embodiment, as data is used togenerate new data as a result of processing, this information of a newlocation of data may be stored and shared between various applications.In at least one embodiment, location of data and a location of updatedor modified data may be part of a definition of how a payload isunderstood within containers.

In at least one embodiment, AI services 1418 may be leveraged to performinferencing services for executing machine learning model(s) associatedwith applications (e.g., tasked with performing one or more processingtasks of an application). In at least one embodiment, AI services 1418may leverage AI system 1424 to execute machine learning model(s) (e.g.,neural networks, such as CNNs) for segmentation, reconstruction, objectdetection, feature detection, classification, and/or other inferencingtasks. In at least one embodiment, applications of deploymentpipeline(s) 1410 may use one or more of output models 1316 from trainingsystem 1304 and/or other models of applications to perform inference onimaging data. In at least one embodiment, two or more examples ofinferencing using application orchestration system 1428 (e.g., ascheduler) may be available. In at least one embodiment, a firstcategory may include a high priority/low latency path that may achievehigher service level agreements, such as for performing inference onurgent requests during an emergency, or for a radiologist duringdiagnosis. In at least one embodiment, a second category may include astandard priority path that may be used for requests that may benon-urgent or where analysis may be performed at a later time. In atleast one embodiment, application orchestration system 1428 maydistribute resources (e.g., services 1320 and/or hardware 1322) based onpriority paths for different inferencing tasks of AI services 1418.

In at least one embodiment, shared storage may be mounted to AI services1418 within system 1400. In at least one embodiment, shared storage mayoperate as a cache (or other storage device type) and may be used toprocess inference requests from applications. In at least oneembodiment, when an inference request is submitted, a request may bereceived by a set of API instances of deployment system 1306, and one ormore instances may be selected (e.g., for best fit, for load balancing,etc.) to process a request. In at least one embodiment, to process arequest, a request may be entered into a database, a machine learningmodel may be located from model registry 1324 if not already in a cache,a validation step may ensure appropriate machine learning model isloaded into a cache (e.g., shared storage), and/or a copy of a model maybe saved to a cache. In at least one embodiment, a scheduler (e.g., ofpipeline manager 1412) may be used to launch an application that isreferenced in a request if an application is not already running or ifthere are not enough instances of an application. In at least oneembodiment, if an inference server is not already launched to execute amodel, an inference server may be launched. Any number of inferenceservers may be launched per model. In at least one embodiment, in a pullmodel, in which inference servers are clustered, models may be cachedwhenever load balancing is advantageous. In at least one embodiment,inference servers may be statically loaded in corresponding, distributedservers.

In at least one embodiment, inferencing may be performed using aninference server that runs in a container. In at least one embodiment,an instance of an inference server may be associated with a model (andoptionally a plurality of versions of a model). In at least oneembodiment, if an instance of an inference server does not exist when arequest to perform inference on a model is received, a new instance maybe loaded. In at least one embodiment, when starting an inferenceserver, a model may be passed to an inference server such that a samecontainer may be used to serve different models so long as inferenceserver is running as a different instance.

In at least one embodiment, during application execution, an inferencerequest for a given application may be received, and a container (e.g.,hosting an instance of an inference server) may be loaded (if notalready), and a start procedure may be called. In at least oneembodiment, pre-processing logic in a container may load, decode, and/orperform any additional pre-processing on incoming data (e.g., using aCPU(s) and/or GPU(s)). In at least one embodiment, once data is preparedfor inference, a container may perform inference as necessary on data.In at least one embodiment, this may include a single inference call onone image (e.g., a hand X-ray), or may require inference on hundreds ofimages (e.g., a chest CT). In at least one embodiment, an applicationmay summarize results before completing, which may include, withoutlimitation, a single confidence score, pixel level-segmentation,voxel-level segmentation, generating a visualization, or generating textto summarize findings. In at least one embodiment, different models orapplications may be assigned different priorities. For example, somemodels may have a real-time (TAT<1 min) priority while others may havelower priority (e.g., TAT<10 min). In at least one embodiment, modelexecution times may be measured from requesting institution or entityand may include partner network traversal time, as well as execution onan inference service.

In at least one embodiment, transfer of requests between services 1320and inference applications may be hidden behind a software developmentkit (SDK), and robust transport may be provide through a queue. In atleast one embodiment, a request will be placed in a queue via an API foran individual application/tenant ID combination and an SDK will pull arequest from a queue and give a request to an application. In at leastone embodiment, a name of a queue may be provided in an environment fromwhere an SDK will pick it up. In at least one embodiment, asynchronouscommunication through a queue may be useful as it may allow any instanceof an application to pick up work as it becomes available. Results maybe transferred back through a queue, to ensure no data is lost. In atleast one embodiment, queues may also provide an ability to segmentwork, as highest priority work may go to a queue with most instances ofan application connected to it, while lowest priority work may go to aqueue with a single instance connected to it that processes tasks in anorder received. In at least one embodiment, an application may run on aGPU-accelerated instance generated in cloud 1426, and an inferenceservice may perform inferencing on a GPU.

In at least one embodiment, visualization services 1420 may be leveragedto generate visualizations for viewing outputs of applications and/ordeployment pipeline(s) 1410. In at least one embodiment, GPUs 1422 maybe leveraged by visualization services 1420 to generate visualizations.In at least one embodiment, rendering effects, such as ray-tracing, maybe implemented by visualization services 1420 to generate higher qualityvisualizations. In at least one embodiment, visualizations may include,without limitation, 2D image renderings, 3D volume renderings, 3D volumereconstruction, 2D tomographic slices, virtual reality displays,augmented reality displays, etc. In at least one embodiment, virtualizedenvironments may be used to generate a virtual interactive display orenvironment (e.g., a virtual environment) for interaction by users of asystem (e.g., doctors, nurses, radiologists, etc.). In at least oneembodiment, visualization services 1420 may include an internalvisualizer, cinematics, and/or other rendering or image processingcapabilities or functionality (e.g., ray tracing, rasterization,internal optics, etc.).

In at least one embodiment, hardware 1322 may include GPUs 1422, AIsystem 1424, cloud 1426, and/or any other hardware used for executingtraining system 1304 and/or deployment system 1306. In at least oneembodiment, GPUs 1422 (e.g., NVIDIA's TESLA and/or QUADRO GPUs) mayinclude any number of GPUs that may be used for executing processingtasks of compute services 1416, AI services 1418, visualization services1420, other services, and/or any of features or functionality ofsoftware 1318. For example, with respect to AI services 1418, GPUs 1422may be used to perform pre-processing on imaging data (or other datatypes used by machine learning models), post-processing on outputs ofmachine learning models, and/or to perform inferencing (e.g., to executemachine learning models). In at least one embodiment, cloud 1426, AIsystem 1424, and/or other components of system 1400 may use GPUs 1422.In at least one embodiment, cloud 1426 may include a GPU-optimizedplatform for deep learning tasks. In at least one embodiment, AI system1424 may use GPUs, and cloud 1426—or at least a portion tasked with deeplearning or inferencing—may be executed using one or more AI systems1424. As such, although hardware 1322 is illustrated as discretecomponents, this is not intended to be limiting, and any components ofhardware 1322 may be combined with, or leveraged by, any othercomponents of hardware 1322.

In at least one embodiment, AI system 1424 may include a purpose-builtcomputing system (e.g., a super-computer or an HPC) configured forinferencing, deep learning, machine learning, and/or other artificialintelligence tasks. In at least one embodiment, AI system 1424 (e.g.,NVIDIA's DGX) may include GPU-optimized software (e.g., a softwarestack) that may be executed using a plurality of GPUs 1422, in additionto CPUs, RAM, storage, and/or other components, features, orfunctionality. In at least one embodiment, one or more AI systems 1424may be implemented in cloud 1426 (e.g., in a data center) for performingsome or all of AI-based processing tasks of system 1400.

In at least one embodiment, cloud 1426 may include a GPU-acceleratedinfrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimizedplatform for executing processing tasks of system 1400. In at least oneembodiment, cloud 1426 may include an AI system(s) 1424 for performingone or more of AI-based tasks of system 1400 (e.g., as a hardwareabstraction and scaling platform). In at least one embodiment, cloud1426 may integrate with application orchestration system 1428 leveragingmultiple GPUs to enable seamless scaling and load balancing between andamong applications and services 1320. In at least one embodiment, cloud1426 may tasked with executing at least some of services 1320 of system1400, including compute services 1416, AI services 1418, and/orvisualization services 1420, as described herein. In at least oneembodiment, cloud 1426 may perform small and large batch inference(e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallelcomputing API and platform 1430 (e.g., NVIDIA's CUDA), executeapplication orchestration system 1428 (e.g., KUBERNETES), provide agraphics rendering API and platform (e.g., for ray-tracing, 2D graphics,3D graphics, and/or other rendering techniques to produce higher qualitycinematics), and/or may provide other functionality for system 1400.

FIG. 15A illustrates a data flow diagram for a process 1500 to train,retrain, or update a machine learning model, in accordance with at leastone embodiment. In at least one embodiment, process 1500 may be executedusing, as a non-limiting example, system 1400 of FIG. 14 . In at leastone embodiment, process 1500 may leverage services 1320 and/or hardware1322 of system 1400, as described herein. In at least one embodiment,refined models 1512 generated by process 1500 may be executed bydeployment system 1306 for one or more containerized applications indeployment pipelines 1410.

In at least one embodiment, model training 1314 may include retrainingor updating an initial model 1504 (e.g., a pre-trained model) using newtraining data (e.g., new input data, such as customer dataset 1506,and/or new ground truth data associated with input data). In at leastone embodiment, to retrain, or update, initial model 1504, output orloss layer(s) of initial model 1504 may be reset, or deleted, and/orreplaced with an updated or new output or loss layer(s). In at least oneembodiment, initial model 1504 may have previously fine-tuned parameters(e.g., weights and/or biases) that remain from prior training, sotraining or retraining 1314 may not take as long or require as muchprocessing as training a model from scratch. In at least one embodiment,during model training 1314, by having reset or replaced output or losslayer(s) of initial model 1504, parameters may be updated and re-tunedfor a new data set based on loss calculations associated with accuracyof output or loss layer(s) at generating predictions on new, customerdataset 1506 (e.g., image data 1308 of FIG. 13 ).

In at least one embodiment, pre-trained models 1406 may be stored in adata store, or registry (e.g., model registry 1324 of FIG. 13 ). In atleast one embodiment, pre-trained models 1406 may have been trained, atleast in part, at one or more facilities other than a facility executingprocess 1500. In at least one embodiment, to protect privacy and rightsof patients, subjects, or clients of different facilities, pre-trainedmodels 1406 may have been trained, on-premise, using customer or patientdata generated on-premise. In at least one embodiment, pre-trainedmodels 1406 may be trained using cloud 1426 and/or other hardware 1322,but confidential, privacy protected patient data may not be transferredto, used by, or accessible to any components of cloud 1426 (or other offpremise hardware). In at least one embodiment, where a pre-trained model1406 is trained at using patient data from more than one facility,pre-trained model 1406 may have been individually trained for eachfacility prior to being trained on patient or customer data from anotherfacility. In at least one embodiment, such as where a customer orpatient data has been released of privacy concerns (e.g., by waiver, forexperimental use, etc.), or where a customer or patient data is includedin a public data set, a customer or patient data from any number offacilities may be used to train pre-trained model 1406 on-premise and/oroff premise, such as in a datacenter or other cloud computinginfrastructure.

In at least one embodiment, when selecting applications for use indeployment pipelines 1410, a user may also select machine learningmodels to be used for specific applications. In at least one embodiment,a user may not have a model for use, so a user may select a pre-trainedmodel 1406 to use with an application. In at least one embodiment,pre-trained model 1406 may not be optimized for generating accurateresults on customer dataset 1506 of a facility of a user (e.g., based onpatient diversity, demographics, types of medical imaging devices used,etc.). In at least one embodiment, prior to deploying pre-trained model1406 into deployment pipeline 1410 for use with an application(s),pre-trained model 1406 may be updated, retrained, and/or fine-tuned foruse at a respective facility.

In at least one embodiment, a user may select pre-trained model 1406that is to be updated, retrained, and/or fine-tuned, and pre-trainedmodel 1406 may be referred to as initial model 1504 for training system1304 within process 1500. In at least one embodiment, customer dataset1506 (e.g., imaging data, genomics data, sequencing data, or other datatypes generated by devices at a facility) may be used to perform modeltraining 1314 (which may include, without limitation, transfer learning)on initial model 1504 to generate refined model 1512. In at least oneembodiment, ground truth data corresponding to customer dataset 1506 maybe generated by training system 1304. In at least one embodiment, groundtruth data may be generated, at least in part, by clinicians,scientists, doctors, practitioners, at a facility (e.g., as labeledclinic data 1312 of FIG. 13 ).

In at least one embodiment, AI-assisted annotation 1310 may be used insome examples to generate ground truth data. In at least one embodiment,AI-assisted annotation 1310 (e.g., implemented using an AI-assistedannotation SDK) may leverage machine learning models (e.g., neuralnetworks) to generate suggested or predicted ground truth data for acustomer dataset. In at least one embodiment, user 1510 may useannotation tools within a user interface (a graphical user interface(GUI)) on computing device 1508.

In at least one embodiment, user 1510 may interact with a GUI viacomputing device 1508 to edit or fine-tune (auto)annotations. In atleast one embodiment, a polygon editing feature may be used to movevertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset 1506 has associatedground truth data, ground truth data (e.g., from AI-assisted annotation,manual labeling, etc.) may be used by during model training 1314 togenerate refined model 1512. In at least one embodiment, customerdataset 1506 may be applied to initial model 1504 any number of times,and ground truth data may be used to update parameters of initial model1504 until an acceptable level of accuracy is attained for refined model1512. In at least one embodiment, once refined model 1512 is generated,refined model 1512 may be deployed within one or more deploymentpipelines 1410 at a facility for performing one or more processing taskswith respect to medical imaging data.

In at least one embodiment, refined model 1512 may be uploaded topre-trained models 1406 in model registry 1324 to be selected by anotherfacility. In at least one embodiment, his process may be completed atany number of facilities such that refined model 1512 may be furtherrefined on new datasets any number of times to generate a more universalmodel.

FIG. 15B is an example illustration of a client-server architecture 1532to enhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment. In at least one embodiment,AI-assisted annotation tools 1536 may be instantiated based on aclient-server architecture 1532. In at least one embodiment, annotationtools 1536 in imaging applications may aid radiologists, for example,identify organs and abnormalities. In at least one embodiment, imagingapplications may include software tools that help user 1510 to identify,as a non-limiting example, a few extreme points on a particular organ ofinterest in raw images 1534 (e.g., in a 3D MRI or CT scan) and receiveauto-annotated results for all 2D slices of a particular organ. In atleast one embodiment, results may be stored in a data store as trainingdata 1538 and used as (for example and without limitation) ground truthdata for training. In at least one embodiment, when computing device1508 sends extreme points for AI-assisted annotation 1310, a deeplearning model, for example, may receive this data as input and returninference results of a segmented organ or abnormality. In at least oneembodiment, pre-instantiated annotation tools, such as AI-AssistedAnnotation Tool 1536B in FIG. 15B, may be enhanced by making API calls(e.g., API Call 1544) to a server, such as an Annotation AssistantServer 1540 that may include a set of pre-trained models 1542 stored inan annotation model registry, for example. In at least one embodiment,an annotation model registry may store pre-trained models 1542 (e.g.,machine learning models, such as deep learning models) that arepre-trained to perform AI-assisted annotation on a particular organ orabnormality. These models may be further updated by using trainingpipelines 1404. In at least one embodiment, pre-installed annotationtools may be improved over time as new labeled clinic data 1312 isadded.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

Autonomous Vehicle

FIG. 16A illustrates an example of an autonomous vehicle 1600, accordingto at least one embodiment. In at least one embodiment, autonomousvehicle 1600 (alternatively referred to herein as “vehicle 1600”) maybe, without limitation, a passenger vehicle, such as a car, a truck, abus, and/or another type of vehicle that accommodates one or morepassengers. In at least one embodiment, vehicle 1 a 00 may be asemi-tractor-trailer truck used for hauling cargo. In at least oneembodiment, vehicle 1 a 00 may be an airplane, robotic vehicle, or otherkind of vehicle.

Autonomous vehicles may be described in terms of automation levels,defined by National Highway Traffic Safety Administration (“NHTSA”), adivision of US Department of Transportation, and Society of AutomotiveEngineers (“SAE”) “Taxonomy and Definitions for Terms Related to DrivingAutomation Systems for On-Road Motor Vehicles” (e.g., Standard No.J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609,published on Sep. 30, 2016, and previous and future versions of thisstandard). In one or more embodiments, vehicle 1600 may be capable offunctionality in accordance with one or more of level 1-level 5 ofautonomous driving levels. For example, in at least one embodiment,vehicle 1600 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending onembodiment.

In at least one embodiment, vehicle 1600 may include, withoutlimitation, components such as a chassis, a vehicle body, wheels (e.g.,2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle.In at least one embodiment, vehicle 1600 may include, withoutlimitation, a propulsion system 1650, such as an internal combustionengine, hybrid electric power plant, an all-electric engine, and/oranother propulsion system type. In at least one embodiment, propulsionsystem 1650 may be connected to a drive train of vehicle 1600, which mayinclude, without limitation, a transmission, to enable propulsion ofvehicle 1600. In at least one embodiment, propulsion system 1650 may becontrolled in response to receiving signals from athrottle/accelerator(s) 1652.

In at least one embodiment, a steering system 1654, which may include,without limitation, a steering wheel, is used to steer a vehicle 1600(e.g., along a desired path or route) when a propulsion system 1650 isoperating (e.g., when vehicle is in motion). In at least one embodiment,a steering system 1654 may receive signals from steering actuator(s)1656. A steering wheel may be optional for full automation (Level 5)functionality. In at least one embodiment, a brake sensor system 1646may be used to operate vehicle brakes in response to receiving signalsfrom brake actuator(s) 1648 and/or brake sensors.

In at least one embodiment, controller(s) 1636, which may include,without limitation, one or more system on chips (“SoCs”) (not shown inFIG. 16A) and/or graphics processing unit(s) (“GPU(s)”), provide signals(e.g., representative of commands) to one or more components and/orsystems of vehicle 1600. For instance, in at least one embodiment,controller(s) 1636 may send signals to operate vehicle brakes via brakeactuator(s) 1648, to operate steering system 1654 via steeringactuator(s) 1656, and/or to operate propulsion system 1650 viathrottle/accelerator(s) 1652. Controller(s) 1636 may include one or moreonboard (e.g., integrated) computing devices (e.g., supercomputers) thatprocess sensor signals, and output operation commands (e.g., signalsrepresenting commands) to enable autonomous driving and/or to assist ahuman driver in driving vehicle 1600. In at least one embodiment,controller(s) 1636 may include a first controller 1636 for autonomousdriving functions, a second controller 1636 for functional safetyfunctions, a third controller 1636 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 1636 forinfotainment functionality, a fifth controller 1636 for redundancy inemergency conditions, and/or other controllers. In at least oneembodiment, a single controller 1636 may handle two or more of abovefunctionalities, two or more controllers 1636 may handle a singlefunctionality, and/or any combination thereof.

In at least one embodiment, controller(s) 1636 provide signals forcontrolling one or more components and/or systems of vehicle 1600 inresponse to sensor data received from one or more sensors (e.g., sensorinputs). In at least one embodiment, sensor data may be received from,for example and without limitation, global navigation satellite systems(“GNSS”) sensor(s) 1658 (e.g., Global Positioning System sensor(s)),RADAR sensor(s) 1660, ultrasonic sensor(s) 1662, LIDAR sensor(s) 1664,inertial measurement unit (“IMU”) sensor(s) 1666 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 1696, stereo camera(s) 1668, wide-view camera(s)1670 (e.g., fisheye cameras), infrared camera(s) 1672, surroundcamera(s) 1674 (e.g., 360 degree cameras), long-range cameras (not shownin FIG. 16A), mid-range camera(s) (not shown in FIG. 16A), speedsensor(s) 1644 (e.g., for measuring speed of vehicle 1600), vibrationsensor(s) 1642, steering sensor(s) 1640, brake sensor(s) (e.g., as partof brake sensor system 1646), and/or other sensor types.

In at least one embodiment, one or more of controller(s) 1636 mayreceive inputs (e.g., represented by input data) from an instrumentcluster 1632 of vehicle 1600 and provide outputs (e.g., represented byoutput data, display data, etc.) via a human-machine interface (“HMI”)display 1634, an audible annunciator, a loudspeaker, and/or via othercomponents of vehicle 1600. In at least one embodiment, outputs mayinclude information such as vehicle velocity, speed, time, map data(e.g., a High Definition map (not shown in FIG. 16A), location data(e.g., vehicle 1600's location, such as on a map), direction, locationof other vehicles (e.g., an occupancy grid), information about objectsand status of objects as perceived by controller(s) 1636, etc. Forexample, in at least one embodiment, HMI display 1634 may displayinformation about presence of one or more objects (e.g., a street sign,caution sign, traffic light changing, etc.), and/or information aboutdriving maneuvers vehicle has made, is making, or will make (e.g.,changing lanes now, taking exit 34B in two miles, etc.).

In at least one embodiment, vehicle 1600 further includes a networkinterface 1624 which may use wireless antenna(s) 1626 and/or modem(s) tocommunicate over one or more networks. For example, in at least oneembodiment, network interface 1624 may be capable of communication overLong-Term Evolution (“LTE”), Wideband Code Division Multiple Access(“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), GlobalSystem for Mobile communication (“GSM”), IMT-CDMA Multi-Carrier(“CDMA2000”), etc. In at least one embodiment, wireless antenna(s) 1626may also enable communication between objects in environment (e.g.,vehicles, mobile devices, etc.), using local area network(s), such asBluetooth, Bluetooth Low Energy (“LE”), Z-Wave, ZigBee, etc., and/or lowpower wide-area network(s) (“LPWANs”), such as LoRaWAN, SigFox, etc.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments. Inat least one embodiment, inference and/or training logic 715 may be usedin system FIG. 16A for inferencing or predicting operations based, atleast in part, on weight parameters calculated using neural networktraining operations, neural network functions and/or architectures, orneural network use cases described herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 16B illustrates an example of camera locations and fields of viewfor autonomous vehicle 1600 of FIG. 16A, according to at least oneembodiment. In at least one embodiment, cameras and respective fields ofview are one example embodiment and are not intended to be limiting. Forinstance, in at least one embodiment, additional and/or alternativecameras may be included and/or cameras may be located at differentlocations on vehicle 1600.

In at least one embodiment, camera types for cameras may include, butare not limited to, digital cameras that may be adapted for use withcomponents and/or systems of vehicle 1600. In at least one embodiment,one or more of camera(s) may operate at automotive safety integritylevel (“ASIL”) B and/or at another ASIL. In at least one embodiment,camera types may be capable of any image capture rate, such as 60 framesper second (fps), 120 fps, 240 fps, etc., depending on embodiment. In atleast one embodiment, cameras may be capable of using rolling shutters,global shutters, another type of shutter, or a combination thereof. Inat least one embodiment, color filter array may include a red clearclear clear (“RCCC”) color filter array, a red clear clear blue (“RCCB”)color filter array, a red blue green clear (“RBGC”) color filter array,a Foveon X3 color filter array, a Bayer sensors (“RGGB”) color filterarray, a monochrome sensor color filter array, and/or another type ofcolor filter array. In at least one embodiment, clear pixel cameras,such as cameras with an RCCC, an RCCB, and/or an RBGC color filterarray, may be used in an effort to increase light sensitivity.

In at least one embodiment, one or more of camera(s) may be used toperform advanced driver assistance systems (“ADAS”) functions (e.g., aspart of a redundant or fail-safe design). For example, in at least oneembodiment, a Multi-Function Mono Camera may be installed to providefunctions including lane departure warning, traffic sign assist andintelligent headlamp control. In at least one embodiment, one or more ofcamera(s) (e.g., all of cameras) may record and provide image data(e.g., video) simultaneously.

In at least one embodiment, one or more of cameras may be mounted in amounting assembly, such as a custom designed (three-dimensional (“3D”)printed) assembly, in order to cut out stray light and reflections fromwithin car (e.g., reflections from dashboard reflected in windshieldmirrors) which may interfere with camera's image data capture abilities.With reference to wing-mirror mounting assemblies, in at least oneembodiment, wing-mirror assemblies may be custom 3D printed so thatcamera mounting plate matches shape of wing-mirror. In at least oneembodiment, camera(s) may be integrated into wing-mirror. For side-viewcameras, camera(s) may also be integrated within four pillars at eachcorner of cabIn at least one embodiment.

In at least one embodiment, cameras with a field of view that includeportions of environment in front of vehicle 1600 (e.g., front-facingcameras) may be used for surround view, to help identify forward facingpaths and obstacles, as well as aid in, with help of one or more ofcontrollers 1636 and/or control SoCs, providing information critical togenerating an occupancy grid and/or determining preferred vehicle paths.In at least one embodiment, front-facing cameras may be used to performmany of same ADAS functions as LIDAR, including, without limitation,emergency braking, pedestrian detection, and collision avoidance. In atleast one embodiment, front-facing cameras may also be used for ADASfunctions and systems including, without limitation, Lane DepartureWarnings (“LDW”), Autonomous Cruise Control (“ACC”), and/or otherfunctions such as traffic sign recognition.

In at least one embodiment, a variety of cameras may be used in afront-facing configuration, including, for example, a monocular cameraplatform that includes a CMOS (“complementary metal oxidesemiconductor”) color imager. In at least one embodiment, wide-viewcamera 1670 may be used to perceive objects coming into view fromperiphery (e.g., pedestrians, crossing traffic or bicycles). Althoughonly one wide-view camera 1670 is illustrated in FIG. 16B, in otherembodiments, there may be any number (including zero) of wide-viewcamera(s) 1670 on vehicle 1600. In at least one embodiment, any numberof long-range camera(s) 1698 (e.g., a long-view stereo camera pair) maybe used for depth-based object detection, especially for objects forwhich a neural network has not yet been trained. In at least oneembodiment, long-range camera(s) 1698 may also be used for objectdetection and classification, as well as basic object tracking.

In at least one embodiment, any number of stereo camera(s) 1668 may alsobe included in a front-facing configuration. In at least one embodiment,one or more of stereo camera(s) 1668 may include an integrated controlunit comprising a scalable processing unit, which may provide aprogrammable logic (“FPGA”) and a multi-core micro-processor with anintegrated Controller Area Network (“CAN”) or Ethernet interface on asingle chip. In at least one embodiment, such a unit may be used togenerate a 3D map of environment of vehicle 1600, including a distanceestimate for all points in image. In at least one embodiment, one ormore of stereo camera(s) 1668 may include, without limitation, compactstereo vision sensor(s) that may include, without limitation, two cameralenses (one each on left and right) and an image processing chip thatmay measure distance from vehicle 1600 to target object and usegenerated information (e.g., metadata) to activate autonomous emergencybraking and lane departure warning functions. In at least oneembodiment, other types of stereo camera(s) 1668 may be used in additionto, or alternatively from, those described herein.

In at least one embodiment, cameras with a field of view that includeportions of environment to side of vehicle 1600 (e.g., side-viewcameras) may be used for surround view, providing information used tocreate and update occupancy grid, as well as to generate side impactcollision warnings. For example, in at least one embodiment, surroundcamera(s) 1674 (e.g., four surround cameras 1674 as illustrated in FIG.16B) could be positioned on vehicle 1600. In at least one embodiment,surround camera(s) 1674 may include, without limitation, any number andcombination of wide-view camera(s) 1670, fisheye camera(s), 360 degreecamera(s), and/or like. For instance, in at least one embodiment, fourfisheye cameras may be positioned on front, rear, and sides of vehicle1600. In at least one embodiment, vehicle 1600 may use three surroundcamera(s) 1674 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround-view camera.

In at least one embodiment, cameras with a field of view that includeportions of environment to rear of vehicle 1600 (e.g., rear-viewcameras) may be used for park assistance, surround view, rear collisionwarnings, and creating and updating occupancy grid. In at least oneembodiment, a wide variety of cameras may be used including, but notlimited to, cameras that are also suitable as a front-facing camera(s)(e.g., long-range cameras 1698 and/or mid-range camera(s) 1676, stereocamera(s) 1668), infrared camera(s) 1672, etc.), as described herein.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are providedbelow. In at least one embodiment, inference and/or training logic 715may be used in system FIG. 16B for inferencing or predicting operationsbased, at least in part, on weight parameters calculated using neuralnetwork training operations, neural network functions and/orarchitectures, or neural network use cases described herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 16C is a block diagram illustrating an example system architecturefor autonomous vehicle 1600 of FIG. 16A, according to at least oneembodiment. In at least one embodiment, each of components, features,and systems of vehicle 1600 in FIG. 16C are illustrated as beingconnected via a bus 1602. In at least one embodiment, bus 1602 mayinclude, without limitation, a CAN data interface (alternativelyreferred to herein as a “CAN bus”). In at least one embodiment, a CANbus may be a network inside vehicle 1600 used to aid in control ofvarious features and functionality of vehicle 1600, such as actuation ofbrakes, acceleration, braking, steering, windshield wipers, etc. In atleast one embodiment, bus 1602 may be configured to have dozens or evenhundreds of nodes, each with its own unique identifier (e.g., a CAN ID).In at least one embodiment, bus 1602 may be read to find steering wheelangle, ground speed, engine revolutions per minute (“RPMs”), buttonpositions, and/or other vehicle status indicators. In at least oneembodiment, bus 1602 may be a CAN bus that is ASIL B compliant.

In at least one embodiment, in addition to, or alternatively from CAN,FlexRay and/or Ethernet may be used. In at least one embodiment, theremay be any number of busses 1602, which may include, without limitation,zero or more CAN busses, zero or more FlexRay busses, zero or moreEthernet busses, and/or zero or more other types of busses using adifferent protocol. In at least one embodiment, two or more busses 1602may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 1602 may be used for collisionavoidance functionality and a second bus 1602 may be used for actuationcontrol. In at least one embodiment, each bus 1602 may communicate withany of components of vehicle 1600, and two or more busses 1602 maycommunicate with same components. In at least one embodiment, each ofany number of system(s) on chip(s) (“SoC(s)”) 1604, each ofcontroller(s) 1636, and/or each computer within vehicle may have accessto same input data (e.g., inputs from sensors of vehicle 1600), and maybe connected to a common bus, such CAN bus.

In at least one embodiment, vehicle 1600 may include one or morecontroller(s) 1636, such as those described herein with respect to FIG.16A. Controller(s) 1636 may be used for a variety of functions. In atleast one embodiment, controller(s) 1636 may be coupled to any ofvarious other components and systems of vehicle 1600, and may be usedfor control of vehicle 1600, artificial intelligence of vehicle 1600,infotainment for vehicle 1600, and/or like.

In at least one embodiment, vehicle 1600 may include any number of SoCs1604. Each of SoCs 1604 may include, without limitation, centralprocessing units (“CPU(s)”) 1606, graphics processing units (“GPU(s)”)1608, processor(s) 1610, cache(s) 1612, accelerator(s) 1614, datastore(s) 1616, and/or other components and features not illustrated. Inat least one embodiment, SoC(s) 1604 may be used to control vehicle 1600in a variety of platforms and systems. For example, in at least oneembodiment, SoC(s) 1604 may be combined in a system (e.g., system ofvehicle 1600) with a High Definition (“HD”) map 1622 which may obtainmap refreshes and/or updates via network interface 1624 from one or moreservers (not shown in FIG. 16C).

In at least one embodiment, CPU(s) 1606 may include a CPU cluster or CPUcomplex (alternatively referred to herein as a “CCPLEX”). In at leastone embodiment, CPU(s) 1606 may include multiple cores and/or level two(“L2”) caches. For instance, in at least one embodiment, CPU(s) 1606 mayinclude eight cores in a coherent multi-processor configuration. In atleast one embodiment, CPU(s) 1606 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). Inat least one embodiment, CPU(s) 1606 (e.g., CCPLEX) may be configured tosupport simultaneous cluster operation enabling any combination ofclusters of CPU(s) 1606 to be active at any given time.

In at least one embodiment, one or more of CPU(s) 1606 may implementpower management capabilities that include, without limitation, one ormore of following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when core is not actively executing instructions dueto execution of Wait for Interrupt (“WFI”)/Wait for Event (“WFE”)instructions; each core may be independently power-gated; each corecluster may be independently clock-gated when all cores are clock-gatedor power-gated; and/or each core cluster may be independentlypower-gated when all cores are power-gated. In at least one embodiment,CPU(s) 1606 may further implement an enhanced algorithm for managingpower states, where allowed power states and expected wakeup times arespecified, and hardware/microcode determines best power state to enterfor core, cluster, and CCPLEX. In at least one embodiment, processingcores may support simplified power state entry sequences in softwarewith work offloaded to microcode.

In at least one embodiment, GPU(s) 1608 may include an integrated GPU(alternatively referred to herein as an “iGPU”). In at least oneembodiment, GPU(s) 1608 may be programmable and may be efficient forparallel workloads. In at least one embodiment, GPU(s) 1608, in at leastone embodiment, may use an enhanced tensor instruction set. In at leastone embodiment, GPU(s) 1608 may include one or more streamingmicroprocessors, where each streaming microprocessor may include a levelone (“L1”) cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of streaming microprocessors may share an L2cache (e.g., an L2 cache with a 512 KB storage capacity). In at leastone embodiment, GPU(s) 1608 may include at least eight streamingmicroprocessors. In at least one embodiment, GPU(s) 1608 may use computeapplication programming interface(s) (API(s)). In at least oneembodiment, GPU(s) 1608 may use one or more parallel computing platformsand/or programming models (e.g., NVIDIA's CUDA).

In at least one embodiment, one or more of GPU(s) 1608 may bepower-optimized for best performance in automotive and embedded usecases. For example, in on embodiment, GPU(s) 1608 could be fabricated ona Fin field-effect transistor (“FinFET”). In at least one embodiment,each streaming microprocessor may incorporate a number ofmixed-precision processing cores partitioned into multiple blocks. Forexample, and without limitation, 64 PF32 cores and 32 PF64 cores couldbe partitioned into four processing blocks. In at least one embodiment,each processing block could be allocated 16 FP32 cores, 8 FP64 cores, 16INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learningmatrix arithmetic, a level zero (“L0”) instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In at leastone embodiment, streaming microprocessors may include independentparallel integer and floating-point data paths to provide for efficientexecution of workloads with a mix of computation and addressingcalculations. In at least one embodiment, streaming microprocessors mayinclude independent thread scheduling capability to enable finer-grainsynchronization and cooperation between parallel threads. In at leastone embodiment, streaming microprocessors may include a combined L1 datacache and shared memory unit in order to improve performance whilesimplifying programming.

In at least one embodiment, one or more of GPU(s) 1608 may include ahigh bandwidth memory (“HBM) and/or a 16 GB HBM2 memory subsystem toprovide, in some examples, about 900 GB/second peak memory bandwidth. Inat least one embodiment, in addition to, or alternatively from, HBMmemory, a synchronous graphics random-access memory (“SGRAM”) may beused, such as a graphics double data rate type five synchronousrandom-access memory (“GDDR5”).

In at least one embodiment, GPU(s) 1608 may include unified memorytechnology. In at least one embodiment, address translation services(“ATS”) support may be used to allow GPU(s) 1608 to access CPU(s) 1606page tables directly. In at least one embodiment, embodiment, whenGPU(s) 1608 memory management unit (“MMU”) experiences a miss, anaddress translation request may be transmitted to CPU(s) 1606. Inresponse, CPU(s) 1606 may look in its page tables forvirtual-to-physical mapping for address and transmits translation backto GPU(s) 1608, in at least one embodiment. In at least one embodiment,unified memory technology may allow a single unified virtual addressspace for memory of both CPU(s) 1606 and GPU(s) 1608, therebysimplifying GPU(s) 1608 programming and porting of applications toGPU(s) 1608.

In at least one embodiment, GPU(s) 1608 may include any number of accesscounters that may keep track of frequency of access of GPU(s) 1608 tomemory of other processors. In at least one embodiment, accesscounter(s) may help ensure that memory pages are moved to physicalmemory of processor that is accessing pages most frequently, therebyimproving efficiency for memory ranges shared between processors.

In at least one embodiment, one or more of SoC(s) 1604 may include anynumber of cache(s) 1612, including those described herein. For example,in at least one embodiment, cache(s) 1612 could include a level three(“L3”) cache that is available to both CPU(s) 1606 and GPU(s) 1608(e.g., that is connected both CPU(s) 1606 and GPU(s) 1608). In at leastone embodiment, cache(s) 1612 may include a write-back cache that maykeep track of states of lines, such as by using a cache coherenceprotocol (e.g., MEI, MESI, MSI, etc.). In at least one embodiment, L3cache may include 4 MB or more, depending on embodiment, althoughsmaller cache sizes may be used.

In at least one embodiment, one or more of SoC(s) 1604 may include oneor more accelerator(s) 1614 (e.g., hardware accelerators, softwareaccelerators, or a combination thereof). In at least one embodiment,SoC(s) 1604 may include a hardware acceleration cluster that may includeoptimized hardware accelerators and/or large on-chip memory. In at leastone embodiment, large on-chip memory (e.g., 4 MB of SRAM), may enablehardware acceleration cluster to accelerate neural networks and othercalculations. In at least one embodiment, hardware acceleration clustermay be used to complement GPU(s) 1608 and to off-load some of tasks ofGPU(s) 1608 (e.g., to free up more cycles of GPU(s) 1608 for performingother tasks). In at least one embodiment, accelerator(s) 1614 could beused for targeted workloads (e.g., perception, convolutional neuralnetworks (“CNNs”), recurrent neural networks (“RNNs”), etc.) that arestable enough to be amenable to acceleration. In at least oneembodiment, a CNN may include a region-based or regional convolutionalneural networks (“RCNNs”) and Fast RCNNs (e.g., as used for objectdetection) or other type of CNN.

In at least one embodiment, accelerator(s) 1614 (e.g., hardwareacceleration cluster) may include a deep learning accelerator(s)(“DLA(s)”). DLA(s) may include, without limitation, one or more Tensorprocessing units (“TPU(s)”) that may be configured to provide anadditional ten trillion operations per second for deep learningapplications and inferencing. In at least one embodiment, TPU(s) may beaccelerators configured to, and optimized for, performing imageprocessing functions (e.g., for CNNs, RCNNs, etc.). DLA(s) may furtherbe optimized for a specific set of neural network types and floatingpoint operations, as well as inferencing. In at least one embodiment,design of DLA(s) may provide more performance per millimeter than atypical general-purpose GPU, and typically vastly exceeds performance ofa CPU. In at least one embodiment, TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions. In at least one embodiment, DLA(s)may quickly and efficiently execute neural networks, especially CNNs, onprocessed or unprocessed data for any of a variety of functions,including, for example and without limitation: a CNN for objectidentification and detection using data from camera sensors; a CNN fordistance estimation using data from camera sensors; a CNN for emergencyvehicle detection and identification and detection using data frommicrophones 1696; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

In at least one embodiment, DLA(s) may perform any function of GPU(s)1608, and by using an inference accelerator, for example, a designer maytarget either DLA(s) or GPU(s) 1608 for any function. For example, in atleast one embodiment, designer may focus processing of CNNs and floatingpoint operations on DLA(s) and leave other functions to GPU(s) 1608and/or other accelerator(s) 1614.

In at least one embodiment, accelerator(s) 1614 (e.g., hardwareacceleration cluster) may include a programmable vision accelerator(s)(“PVA”), which may alternatively be referred to herein as a computervision accelerator. In at least one embodiment, PVA(s) may be designedand configured to accelerate computer vision algorithms for advanceddriver assistance system (“ADAS”) 1638, autonomous driving, augmentedreality (“AR”) applications, and/or virtual reality (“VR”) applications.PVA(s) may provide a balance between performance and flexibility. Forexample, in at least one embodiment, each PVA(s) may include, forexample and without limitation, any number of reduced instruction setcomputer (“RISC”) cores, direct memory access (“DMA”), and/or any numberof vector processors.

In at least one embodiment, RISC cores may interact with image sensors(e.g., image sensors of any of cameras described herein), image signalprocessor(s), and/or like. In at least one embodiment, each of RISCcores may include any amount of memory. In at least one embodiment, RISCcores may use any of a number of protocols, depending on embodiment. Inat least one embodiment, RISC cores may execute a real-time operatingsystem (“RTOS”). In at least one embodiment, RISC cores may beimplemented using one or more integrated circuit devices, applicationspecific integrated circuits (“ASICs”), and/or memory devices. Forexample, in at least one embodiment, RISC cores could include aninstruction cache and/or a tightly coupled RAM.

In at least one embodiment, DMA may enable components of PVA(s) toaccess system memory independently of CPU(s) 1606. In at least oneembodiment, DMA may support any number of features used to provideoptimization to PVA including, but not limited to, supportingmulti-dimensional addressing and/or circular addressing. In at least oneembodiment, DMA may support up to six or more dimensions of addressing,which may include, without limitation, block width, block height, blockdepth, horizontal block stepping, vertical block stepping, and/or depthstepping.

In at least one embodiment, vector processors may be programmableprocessors that may be designed to efficiently and flexibly executeprogramming for computer vision algorithms and provide signal processingcapabilities. In at least one embodiment, PVA may include a PVA core andtwo vector processing subsystem partitions. In at least one embodiment,PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMAengines), and/or other peripherals. In at least one embodiment, vectorprocessing subsystem may operate as primary processing engine of PVA,and may include a vector processing unit (“VPU”), an instruction cache,and/or vector memory (e.g., “VMEM”). In at least one embodiment, VPU mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (“SIMD”), very long instruction word (“VLIW”)digital signal processor. In at least one embodiment, a combination ofSIMD and VLIW may enhance throughput and speed.

In at least one embodiment, each of vector processors may include aninstruction cache and may be coupled to dedicated memory. As a result,in at least one embodiment, each of vector processors may be configuredto execute independently of other vector processors. In at least oneembodiment, vector processors that are included in a particular PVA maybe configured to employ data parallelism. For instance, in at least oneembodiment, plurality of vector processors included in a single PVA mayexecute same computer vision algorithm, but on different regions of animage. In at least one embodiment, vector processors included in aparticular PVA may simultaneously execute different computer visionalgorithms, on same image, or even execute different algorithms onsequential images or portions of an image. In at least one embodiment,among other things, any number of PVAs may be included in hardwareacceleration cluster and any number of vector processors may be includedin each of PVAs. In at least one embodiment, PVA(s) may includeadditional error correcting code (“ECC”) memory, to enhance overallsystem safety.

In at least one embodiment, accelerator(s) 1614 (e.g., hardwareacceleration cluster) may include a computer vision network on-chip andstatic random-access memory (“SRAM”), for providing a high-bandwidth,low latency SRAM for accelerator(s) 1614. In at least one embodiment,on-chip memory may include at least 4 MB SRAM, consisting of, forexample and without limitation, eight field-configurable memory blocks,that may be accessible by both PVA and DLA. In at least one embodiment,each pair of memory blocks may include an advanced peripheral bus(“APB”) interface, configuration circuitry, a controller, and amultiplexer. In at least one embodiment, any type of memory may be used.In at least one embodiment, PVA and DLA may access memory via a backbonethat provides PVA and DLA with high-speed access to memory. In at leastone embodiment, backbone may include a computer vision network on-chipthat interconnects PVA and DLA to memory (e.g., using APB).

In at least one embodiment, computer vision network on-chip may includean interface that determines, before transmission of any controlsignal/address/data, that both PVA and DLA provide ready and validsignals. In at least one embodiment, an interface may provide forseparate phases and separate channels for transmitting controlsignals/addresses/data, as well as burst-type communications forcontinuous data transfer. In at least one embodiment, an interface maycomply with International Organization for Standardization (“ISO”) 26262or International Electrotechnical Commission (“IEC”) 61508 standards,although other standards and protocols may be used.

In at least one embodiment, one or more of SoC(s) 1604 may include areal-time ray-tracing hardware accelerator. In at least one embodiment,real-time ray-tracing hardware accelerator may be used to quickly andefficiently determine positions and extents of objects (e.g., within aworld model), to generate real-time visualization simulations, for RADARsignal interpretation, for sound propagation synthesis and/or analysis,for simulation of SONAR systems, for general wave propagationsimulation, for comparison to LIDAR data for purposes of localizationand/or other functions, and/or for other uses.

In at least one embodiment, accelerator(s) 1614 (e.g., hardwareaccelerator cluster) have a wide array of uses for autonomous driving.In at least one embodiment, PVA may be a programmable vision acceleratorthat may be used for key processing stages in ADAS and autonomousvehicles. In at least one embodiment, PVA's capabilities are a goodmatch for algorithmic domains needing predictable processing, at lowpower and low latency. In other words, PVA performs well on semi-denseor dense regular computation, even on small data sets, which needpredictable run-times with low latency and low power. In at least oneembodiment, autonomous vehicles, such as vehicle 1600, PVAs are designedto run classic computer vision algorithms, as they are efficient atobject detection and operating on integer math.

For example, according to at least one embodiment of technology, PVA isused to perform computer stereo vision. In at least one embodiment,semi-global matching-based algorithm may be used in some examples,although this is not intended to be limiting. In at least oneembodiment, applications for Level 3-5 autonomous driving use motionestimation/stereo matching on-the-fly (e.g., structure from motion,pedestrian recognition, lane detection, etc.). In at least oneembodiment, PVA may perform computer stereo vision function on inputsfrom two monocular cameras.

In at least one embodiment, PVA may be used to perform dense opticalflow. For example, in at least one embodiment, PVA could process rawRADAR data (e.g., using a 4D Fast Fourier Transform) to provideprocessed RADAR data. In at least one embodiment, PVA is used for timeof flight depth processing, by processing raw time of flight data toprovide processed time of flight data, for example.

In at least one embodiment, DLA may be used to run any type of networkto enhance control and driving safety, including for example and withoutlimitation, a neural network that outputs a measure of confidence foreach object detection. In at least one embodiment, confidence may berepresented or interpreted as a probability, or as providing a relative“weight” of each detection compared to other detections. In at least oneembodiment, confidence enables a system to make further decisionsregarding which detections should be considered as true positivedetections rather than false positive detections. For example, In atleast one embodiment, a system may set a threshold value for confidenceand consider only detections exceeding threshold value as true positivedetections. In an embodiment in which an automatic emergency braking(“AEB”) system is used, false positive detections would cause vehicle toautomatically perform emergency braking, which is obviously undesirable.In at least one embodiment, highly confident detections may beconsidered as triggers for AEB. In at least one embodiment, DLA may runa neural network for regressing confidence value. In at least oneembodiment, neural network may take as its input at least some subset ofparameters, such as bounding box dimensions, ground plane estimateobtained (e.g. from another subsystem), output from IMU sensor(s) 1666that correlates with vehicle 1600 orientation, distance, 3D locationestimates of object obtained from neural network and/or other sensors(e.g., LIDAR sensor(s) 1664 or RADAR sensor(s) 1660), among others.

In at least one embodiment, one or more of SoC(s) 1604 may include datastore(s) 1616 (e.g., memory). In at least one embodiment, data store(s)1616 may be on-chip memory of SoC(s) 1604, which may store neuralnetworks to be executed on GPU(s) 1608 and/or DLA. In at least oneembodiment, data store(s) 1616 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. In atleast one embodiment, data store(s) 1616 may comprise L2 or L3 cache(s).

In at least one embodiment, one or more of SoC(s) 1604 may include anynumber of processor(s) 1610 (e.g., embedded processors). In at least oneembodiment, processor(s) 1610 may include a boot and power managementprocessor that may be a dedicated processor and subsystem to handle bootpower and management functions and related security enforcement. In atleast one embodiment, boot and power management processor may be a partof SoC(s) 1604 boot sequence and may provide runtime power managementservices. In at least one embodiment, boot power and managementprocessor may provide clock and voltage programming, assistance insystem low power state transitions, management of SoC(s) 1604 thermalsand temperature sensors, and/or management of SoC(s) 1604 power states.In at least one embodiment, each temperature sensor may be implementedas a ring-oscillator whose output frequency is proportional totemperature, and SoC(s) 1604 may use ring-oscillators to detecttemperatures of CPU(s) 1606, GPU(s) 1608, and/or accelerator(s) 1614. Inat least one embodiment, if temperatures are determined to exceed athreshold, then boot and power management processor may enter atemperature fault routine and put SoC(s) 1604 into a lower power stateand/or put vehicle 1600 into a chauffeur to safe stop mode (e.g., bringvehicle 1600 to a safe stop).

In at least one embodiment, processor(s) 1610 may further include a setof embedded processors that may serve as an audio processing engine. Inat least one embodiment, audio processing engine may be an audiosubsystem that enables full hardware support for multi-channel audioover multiple interfaces, and a broad and flexible range of audio I/Ointerfaces. In at least one embodiment, audio processing engine is adedicated processor core with a digital signal processor with dedicatedRAM.

In at least one embodiment, processor(s) 1610 may further include analways on processor engine that may provide necessary hardware featuresto support low power sensor management and wake use cases. In at leastone embodiment, always on processor engine may include, withoutlimitation, a processor core, a tightly coupled RAM, supportingperipherals (e.g., timers and interrupt controllers), various I/Ocontroller peripherals, and routing logic.

In at least one embodiment, processor(s) 1610 may further include asafety cluster engine that includes, without limitation, a dedicatedprocessor subsystem to handle safety management for automotiveapplications. In at least one embodiment, safety cluster engine mayinclude, without limitation, two or more processor cores, a tightlycoupled RAM, support peripherals (e.g., timers, an interrupt controller,etc.), and/or routing logic. In a safety mode, two or more cores mayoperate, in at least one embodiment, in a lockstep mode and function asa single core with comparison logic to detect any differences betweentheir operations. In at least one embodiment, processor(s) 1610 mayfurther include a real-time camera engine that may include, withoutlimitation, a dedicated processor subsystem for handling real-timecamera management. In at least one embodiment, processor(s) 1610 mayfurther include a high-dynamic range signal processor that may include,without limitation, an image signal processor that is a hardware enginethat is part of camera processing pipeline.

In at least one embodiment, processor(s) 1610 may include a video imagecompositor that may be a processing block (e.g., implemented on amicroprocessor) that implements video post-processing functions neededby a video playback application to produce final image for playerwindow. In at least one embodiment, video image compositor may performlens distortion correction on wide-view camera(s) 1670, surroundcamera(s) 1674, and/or on in-cabin monitoring camera sensor(s). In atleast one embodiment, in-cabin monitoring camera sensor(s) arepreferably monitored by a neural network running on another instance ofSoC(s) 1604, configured to identify in cabin events and respondaccordingly. In at least one embodiment, an in-cabin system may perform,without limitation, lip reading to activate cellular service and place aphone call, dictate emails, change vehicle's destination, activate orchange vehicle's infotainment system and settings, or providevoice-activated web surfing. In at least one embodiment, certainfunctions are available to driver when vehicle is operating in anautonomous mode and are disabled otherwise.

In at least one embodiment, video image compositor may include enhancedtemporal noise reduction for both spatial and temporal noise reduction.For example, in at least one embodiment, where motion occurs in a video,noise reduction weights spatial information appropriately, decreasingweight of information provided by adjacent frames. In at least oneembodiment, where an image or portion of an image does not includemotion, temporal noise reduction performed by video image compositor mayuse information from previous image to reduce noise in current image.

In at least one embodiment, video image compositor may also beconfigured to perform stereo rectification on input stereo lens frames.In at least one embodiment, video image compositor may further be usedfor user interface composition when operating system desktop is in use,and GPU(s) 1608 are not required to continuously render new surfaces. Inat least one embodiment, when GPU(s) 1608 are powered on and activedoing 3D rendering, video image compositor may be used to offload GPU(s)1608 to improve performance and responsiveness.

In at least one embodiment, one or more of SoC(s) 1604 may furtherinclude a mobile industry processor interface (“MIPI”) camera serialinterface for receiving video and input from cameras, a high-speedinterface, and/or a video input block that may be used for camera andrelated pixel input functions. In at least one embodiment, one or moreof SoC(s) 1604 may further include an input/output controller(s) thatmay be controlled by software and may be used for receiving I/O signalsthat are uncommitted to a specific role.

In at least one embodiment, one or more of SoC(s) 1604 may furtherinclude a broad range of peripheral interfaces to enable communicationwith peripherals, audio encoders/decoders (“codecs”), power management,and/or other devices. SoC(s) 1604 may be used to process data fromcameras (e.g., connected over Gigabit Multimedia Serial Link andEthernet), sensors (e.g., LIDAR sensor(s) 1664, RADAR sensor(s) 1660,etc. that may be connected over Ethernet), data from bus 1602 (e.g.,speed of vehicle 1600, steering wheel position, etc.), data from GNSSsensor(s) 1658 (e.g., connected over Ethernet or CAN bus), etc. In atleast one embodiment, one or more of SoC(s) 1604 may further includededicated high-performance mass storage controllers that may includetheir own DMA engines, and that may be used to free CPU(s) 1606 fromroutine data management tasks.

In at least one embodiment, SoC(s) 1604 may be an end-to-end platformwith a flexible architecture that spans automation levels 3-5, therebyproviding a comprehensive functional safety architecture that leveragesand makes efficient use of computer vision and ADAS techniques fordiversity and redundancy, provides a platform for a flexible, reliabledriving software stack, along with deep learning tools. In at least oneembodiment, SoC(s) 1604 may be faster, more reliable, and even moreenergy-efficient and space-efficient than conventional systems. Forexample, in at least one embodiment, accelerator(s) 1614, when combinedwith CPU(s) 1606, GPU(s) 1608, and data store(s) 1616, may provide for afast, efficient platform for level 3-5 autonomous vehicles.

In at least one embodiment, computer vision algorithms may be executedon CPUs, which may be configured using high-level programming language,such as C programming language, to execute a wide variety of processingalgorithms across a wide variety of visual data. However, in at leastone embodiment, CPUs are oftentimes unable to meet performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In at least oneembodiment, many CPUs are unable to execute complex object detectionalgorithms in real-time, which is used in in-vehicle ADAS applicationsand in practical Level 3-5 autonomous vehicles.

Embodiments described herein allow for multiple neural networks to beperformed simultaneously and/or sequentially, and for results to becombined together to enable Level 3-5 autonomous driving functionality.For example, in at least one embodiment, a CNN executing on DLA ordiscrete GPU (e.g., GPU(s) 1620) may include text and word recognition,allowing supercomputer to read and understand traffic signs, includingsigns for which neural network has not been specifically trained. In atleast one embodiment, DLA may further include a neural network that isable to identify, interpret, and provide semantic understanding of sign,and to pass that semantic understanding to path planning modules runningon CPU Complex.

In at least one embodiment, multiple neural networks may be runsimultaneously, as for Level 3, 4, or 5 driving. For example, in atleast one embodiment, a warning sign consisting of “Caution: flashinglights indicate icy conditions,” along with an electric light, may beindependently or collectively interpreted by several neural networks. Inat least one embodiment, a sign itself may be identified as a trafficsign by a first deployed neural network (e.g., a neural network that hasbeen trained) and a text “flashing lights indicate icy conditions” maybe interpreted by a second deployed neural network, which informsvehicle's path planning software (preferably executing on CPU Complex)that when flashing lights are detected, icy conditions exist. In atleast one embodiment, a flashing light may be identified by operating athird deployed neural network over multiple frames, informing vehicle'spath-planning software of presence (or absence) of flashing lights. Inat least one embodiment, all three neural networks may runsimultaneously, such as within DLA and/or on GPU(s) 1608.

In at least one embodiment, a CNN for facial recognition and vehicleowner identification may use data from camera sensors to identifypresence of an authorized driver and/or owner of vehicle 1600. In atleast one embodiment, an always on sensor processing engine may be usedto unlock vehicle when owner approaches driver door and turn on lights,and, in security mode, to disable vehicle when owner leaves vehicle. Inthis way, SoC(s) 1604 provide for security against theft and/orcarjacking.

In at least one embodiment, a CNN for emergency vehicle detection andidentification may use data from microphones 1696 to detect and identifyemergency vehicle sirens. In at least one embodiment, SoC(s) 1604 useCNN for classifying environmental and urban sounds, as well asclassifying visual data. In at least one embodiment, CNN running on DLAis trained to identify relative closing speed of emergency vehicle(e.g., by using Doppler effect). In at least one embodiment, CNN mayalso be trained to identify emergency vehicles specific to local area inwhich vehicle is operating, as identified by GNSS sensor(s) 1658. In atleast one embodiment, when operating in Europe, CNN will seek to detectEuropean sirens, and when in United States CNN will seek to identifyonly North American sirens. In at least one embodiment, once anemergency vehicle is detected, a control program may be used to executean emergency vehicle safety routine, slowing vehicle, pulling over toside of road, parking vehicle, and/or idling vehicle, with assistance ofultrasonic sensor(s) 1662, until emergency vehicle(s) passes.

In at least one embodiment, vehicle 1600 may include CPU(s) 1618 (e.g.,discrete CPU(s), or dCPU(s)), that may be coupled to SoC(s) 1604 via ahigh-speed interconnect (e.g., PCIe). In at least one embodiment, CPU(s)1618 may include an X86 processor, for example. CPU(s) 1618 may be usedto perform any of a variety of functions, including arbitratingpotentially inconsistent results between ADAS sensors and SoC(s) 1604,and/or monitoring status and health of controller(s) 1636 and/or aninfotainment system on a chip (“infotainment SoC”) 1630, for example.

In at least one embodiment, vehicle 1600 may include GPU(s) 1620 (e.g.,discrete GPU(s), or dGPU(s)), that may be coupled to SoC(s) 1604 via ahigh-speed interconnect (e.g., NVIDIA's NVLINK). In at least oneembodiment, GPU(s) 1620 may provide additional artificial intelligencefunctionality, such as by executing redundant and/or different neuralnetworks, and may be used to train and/or update neural networks basedat least in part on input (e.g., sensor data) from sensors of vehicle1600.

In at least one embodiment, vehicle 1600 may further include networkinterface 1624 which may include, without limitation, wirelessantenna(s) 1626 (e.g., one or more wireless antennas 1626 for differentcommunication protocols, such as a cellular antenna, a Bluetoothantenna, etc.). In at least one embodiment, network interface 1624 maybe used to enable wireless connectivity over Internet with cloud (e.g.,with server(s) and/or other network devices), with other vehicles,and/or with computing devices (e.g., client devices of passengers). Inat least one embodiment, to communicate with other vehicles, a directlink may be established between vehicle 160 and other vehicle and/or anindirect link may be established (e.g., across networks and overInternet). In at least one embodiment, direct links may be providedusing a vehicle-to-vehicle communication link. vehicle-to-vehiclecommunication link may provide vehicle 1600 information about vehiclesin proximity to vehicle 1600 (e.g., vehicles in front of, on side of,and/or behind vehicle 1600). In at least one embodiment, aforementionedfunctionality may be part of a cooperative adaptive cruise controlfunctionality of vehicle 1600.

In at least one embodiment, network interface 1624 may include a SoCthat provides modulation and demodulation functionality and enablescontroller(s) 1636 to communicate over wireless networks. In at leastone embodiment, network interface 1624 may include a radio frequencyfront-end for up-conversion from baseband to radio frequency, and downconversion from radio frequency to baseband. In at least one embodiment,frequency conversions may be performed in any technically feasiblefashion. For example, frequency conversions could be performed throughwell-known processes, and/or using super-heterodyne processes. In atleast one embodiment, radio frequency front end functionality may beprovided by a separate chip. In at least one embodiment, networkinterface may include wireless functionality for communicating over LTE,WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave,ZigBee, LoRaWAN, and/or other wireless protocols.

In at least one embodiment, vehicle 1600 may further include datastore(s) 1628 which may include, without limitation, off-chip (e.g., offSoC(s) 1604) storage. In at least one embodiment, data store(s) 1628 mayinclude, without limitation, one or more storage elements including RAM,SRAM, dynamic random-access memory (“DRAM”), video random-access memory(“VRAM”), Flash, hard disks, and/or other components and/or devices thatmay store at least one bit of data.

In at least one embodiment, vehicle 1600 may further include GNSSsensor(s) 1658 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. In at least one embodiment, any number of GNSS sensor(s) 1658may be used, including, for example and without limitation, a GPS usinga USB connector with an Ethernet to Serial (e.g., RS-232) bridge.

In at least one embodiment, vehicle 1600 may further include RADARsensor(s) 1660. RADAR sensor(s) 1660 may be used by vehicle 1600 forlong-range vehicle detection, even in darkness and/or severe weatherconditions. In at least one embodiment, RADAR functional safety levelsmay be ASIL B. RADAR sensor(s) 1660 may use CAN and/or bus 1602 (e.g.,to transmit data generated by RADAR sensor(s) 1660) for control and toaccess object tracking data, with access to Ethernet to access raw datain some examples. In at least one embodiment, wide variety of RADARsensor types may be used. For example, and without limitation, RADARsensor(s) 1660 may be suitable for front, rear, and side RADAR use. Inat least one embodiment, one or more of RADAR sensors(s) 1660 are PulseDoppler RADAR sensor(s).

In at least one embodiment, RADAR sensor(s) 1660 may include differentconfigurations, such as long-range with narrow field of view,short-range with wide field of view, short-range side coverage, etc. Inat least one embodiment, long-range RADAR may be used for adaptivecruise control functionality. In at least one embodiment, long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. In at least oneembodiment, RADAR sensor(s) 1660 may help in distinguishing betweenstatic and moving objects, and may be used by ADAS system 1638 foremergency brake assist and forward collision warning. Sensors 1660(s)included in a long-range RADAR system may include, without limitation,monostatic multimodal RADAR with multiple (e.g., six or more) fixedRADAR antennae and a high-speed CAN and FlexRay interface. In at leastone embodiment, with six antennae, central four antennae may create afocused beam pattern, designed to record vehicle 1600's surroundings athigher speeds with minimal interference from traffic in adjacent lanes.In at least one embodiment, other two antennae may expand field of view,making it possible to quickly detect vehicles entering or leavingvehicle 1600's lane.

In at least one embodiment, mid-range RADAR systems may include, as anexample, a range of up to 160 m (front) or 80 m (rear), and a field ofview of up to 42 degrees (front) or 150 degrees (rear). In at least oneembodiment, short-range RADAR systems may include, without limitation,any number of RADAR sensor(s) 1660 designed to be installed at both endsof rear bumper. When installed at both ends of rear bumper, in at leastone embodiment, a RADAR sensor system may create two beams thatconstantly monitor blind spot in rear and next to vehicle. In at leastone embodiment, short-range RADAR systems may be used in ADAS system1638 for blind spot detection and/or lane change assist.

In at least one embodiment, vehicle 1600 may further include ultrasonicsensor(s) 1662. Ultrasonic sensor(s) 1662, which may be positioned atfront, back, and/or sides of vehicle 1600, may be used for park assistand/or to create and update an occupancy grid. In at least oneembodiment, a wide variety of ultrasonic sensor(s) 1662 may be used, anddifferent ultrasonic sensor(s) 1662 may be used for different ranges ofdetection (e.g., 2.5 m, 4 m). In at least one embodiment, ultrasonicsensor(s) 1662 may operate at functional safety levels of ASIL B.

In at least one embodiment, vehicle 1600 may include LIDAR sensor(s)1664. LIDAR sensor(s) 1664 may be used for object and pedestriandetection, emergency braking, collision avoidance, and/or otherfunctions. In at least one embodiment, LIDAR sensor(s) 1664 may befunctional safety level ASIL B. In at least one embodiment, vehicle 1600may include multiple LIDAR sensors 1664 (e.g., two, four, six, etc.)that may use Ethernet (e.g., to provide data to a Gigabit Ethernetswitch).

In at least one embodiment, LIDAR sensor(s) 1664 may be capable ofproviding a list of objects and their distances for a 360-degree fieldof view. In at least one embodiment, commercially available LIDARsensor(s) 1664 may have an advertised range of approximately 100 m, withan accuracy of 2 cm-3 cm, and with support for a 100 Mbps Ethernetconnection, for example. In at least one embodiment, one or morenon-protruding LIDAR sensors 1664 may be used. In such an embodiment,LIDAR sensor(s) 1664 may be implemented as a small device that may beembedded into front, rear, sides, and/or corners of vehicle 1600. In atleast one embodiment, LIDAR sensor(s) 1664, in such an embodiment, mayprovide up to a 120-degree horizontal and 35-degree verticalfield-of-view, with a 200 m range even for low-reflectivity objects. Inat least one embodiment, front-mounted LIDAR sensor(s) 1664 may beconfigured for a horizontal field of view between 45 degrees and 135degrees.

In at least one embodiment, LIDAR technologies, such as 3D flash LIDAR,may also be used. 3D Flash LIDAR uses a flash of a laser as atransmission source, to illuminate surroundings of vehicle 1600 up toapproximately 200 m. In at least one embodiment, a flash LIDAR unitincludes, without limitation, a receptor, which records laser pulsetransit time and reflected light on each pixel, which in turncorresponds to range from vehicle 1600 to objects. In at least oneembodiment, flash LIDAR may allow for highly accurate anddistortion-free images of surroundings to be generated with every laserflash. In at least one embodiment, four flash LIDAR sensors may bedeployed, one at each side of vehicle 1600. In at least one embodiment,3D flash LIDAR systems include, without limitation, a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). In at least one embodiment, flash LIDARdevice(s) may use a 5 nanosecond class I (eye-safe) laser pulse perframe and may capture reflected laser light in form of 3D range pointclouds and co-registered intensity data.

In at least one embodiment, vehicle may further include IMU sensor(s)1666. In at least one embodiment, IMU sensor(s) 1666 may be located at acenter of rear axle of vehicle 1600, in at least one embodiment. In atleast one embodiment, IMU sensor(s) 1666 may include, for example andwithout limitation, accelerometer(s), magnetometer(s), gyroscope(s),magnetic compass(es), and/or other sensor types. In at least oneembodiment, such as in six-axis applications, IMU sensor(s) 1666 mayinclude, without limitation, accelerometers and gyroscopes. In at leastone embodiment, such as in nine-axis applications, IMU sensor(s) 1666may include, without limitation, accelerometers, gyroscopes, andmagnetometers.

In at least one embodiment, IMU sensor(s) 1666 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(“GPS/INS”) that combines micro-electro-mechanical systems (“MEMS”)inertial sensors, a high-sensitivity GPS receiver, and advanced Kalmanfiltering algorithms to provide estimates of position, velocity, andattitude. In at least one embodiment, IMU sensor(s) 1666 may enablevehicle 1600 to estimate heading without requiring input from a magneticsensor by directly observing and correlating changes in velocity fromGPS to IMU sensor(s) 1666. In at least one embodiment, IMU sensor(s)1666 and GNSS sensor(s) 1658 may be combined in a single integratedunit.

In at least one embodiment, vehicle 1600 may include microphone(s) 1696placed in and/or around vehicle 1600. In at least one embodiment,microphone(s) 1696 may be used for emergency vehicle detection andidentification, among other things.

In at least one embodiment, vehicle 1600 may further include any numberof camera types, including stereo camera(s) 1668, wide-view camera(s)1670, infrared camera(s) 1672, surround camera(s) 1674, long-rangecamera(s) 1698, mid-range camera(s) 1676, and/or other camera types. Inat least one embodiment, cameras may be used to capture image dataaround an entire periphery of vehicle 1600. In at least one embodiment,types of cameras used depends on vehicle 1600. In at least oneembodiment, any combination of camera types may be used to providenecessary coverage around vehicle 1600. In at least one embodiment,number of cameras may differ depending on embodiment. For example, in atleast one embodiment, vehicle 1600 could include six cameras, sevencameras, ten cameras, twelve cameras, or another number of cameras.Cameras may support, as an example and without limitation, GigabitMultimedia Serial Link (“GMSL”) and/or Gigabit Ethernet. In at least oneembodiment, each of camera(s) is described with more detail previouslyherein with respect to FIG. 16A and FIG. 16B.

In at least one embodiment, vehicle 1600 may further include vibrationsensor(s) 1642. In at least one embodiment, vibration sensor(s) 1642 maymeasure vibrations of components of vehicle 1600, such as axle(s). Forexample, in at least one embodiment, changes in vibrations may indicatea change in road surfaces. In at least one embodiment, when two or morevibration sensors 1642 are used, differences between vibrations may beused to determine friction or slippage of road surface (e.g., whendifference in vibration is between a power-driven axle and a freelyrotating axle).

In at least one embodiment, vehicle 1600 may include ADAS system 1638.ADAS system 1638 may include, without limitation, a SoC, in someexamples. In at least one embodiment, ADAS system 1638 may include,without limitation, any number and combination of anautonomous/adaptive/automatic cruise control (“ACC”) system, acooperative adaptive cruise control (“CACC”) system, a forward crashwarning (“FCW”) system, an automatic emergency braking (“AEB”) system, alane departure warning (“LDW)” system, a lane keep assist (“LKA”)system, a blind spot warning (“BSW”) system, a rear cross-trafficwarning (“RCTW”) system, a collision warning (“CW”) system, a lanecentering (“LC”) system, and/or other systems, features, and/orfunctionality.

In at least one embodiment, ACC system may use RADAR sensor(s) 1660,LIDAR sensor(s) 1664, and/or any number of camera(s). In at least oneembodiment, ACC system may include a longitudinal ACC system and/or alateral ACC system. In at least one embodiment, longitudinal ACC systemmonitors and controls distance to vehicle immediately ahead of vehicle1600 and automatically adjust speed of vehicle 1600 to maintain a safedistance from vehicles ahead. In at least one embodiment, lateral ACCsystem performs distance keeping, and advises vehicle 1600 to changelanes when necessary. In at least one embodiment, lateral ACC is relatedto other ADAS applications such as LC and CW.

In at least one embodiment, CACC system uses information from othervehicles that may be received via network interface 1624 and/or wirelessantenna(s) 1626 from other vehicles via a wireless link, or indirectly,over a network connection (e.g., over Internet). In at least oneembodiment, direct links may be provided by a vehicle-to-vehicle (“V2V”)communication link, while indirect links may be provided by aninfrastructure-to-vehicle (“I2V”) communication link. In general, V2Vcommunication concept provides information about immediately precedingvehicles (e.g., vehicles immediately ahead of and in same lane asvehicle 1600), while I2V communication concept provides informationabout traffic further ahead. In at least one embodiment, CACC system mayinclude either or both I2V and V2V information sources. In at least oneembodiment, given information of vehicles ahead of vehicle 1600, CACCsystem may be more reliable and it has potential to improve traffic flowsmoothness and reduce congestion on road.

In at least one embodiment, FCW system is designed to alert driver to ahazard, so that driver may take corrective action. In at least oneembodiment, FCW system uses a front-facing camera and/or RADAR sensor(s)1660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component. In at least one embodiment, FCW system mayprovide a warning, such as in form of a sound, visual warning, vibrationand/or a quick brake pulse.

In at least one embodiment, AEB system detects an impending forwardcollision with another vehicle or other object, and may automaticallyapply brakes if driver does not take corrective action within aspecified time or distance parameter. In at least one embodiment, AEBsystem may use front-facing camera(s) and/or RADAR sensor(s) 1660,coupled to a dedicated processor, DSP, FPGA, and/or ASIC. In at leastone embodiment, when AEB system detects a hazard, AEB system typicallyfirst alerts driver to take corrective action to avoid collision and, ifdriver does not take corrective action, AEB system may automaticallyapply brakes in an effort to prevent, or at least mitigate, impact ofpredicted collision. In at least one embodiment, AEB system, may includetechniques such as dynamic brake support and/or crash imminent braking.

In at least one embodiment, LDW system provides visual, audible, and/ortactile warnings, such as steering wheel or seat vibrations, to alertdriver when vehicle 1600 crosses lane markings. In at least oneembodiment, LDW system does not activate when driver indicates anintentional lane departure, by activating a turn signal. In at least oneembodiment, LDW system may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent. In at least one embodiment, LKA system is a variation of LDWsystem. LKA system provides steering input or braking to correct vehicle1600 if vehicle 1600 starts to exit lane.

In at least one embodiment, BSW system detects and warns driver ofvehicles in an automobile's blind spot. In at least one embodiment, BSWsystem may provide a visual, audible, and/or tactile alert to indicatethat merging or changing lanes is unsafe. In at least one embodiment,BSW system may provide an additional warning when driver uses a turnsignal. In at least one embodiment, BSW system may use rear-side facingcamera(s) and/or RADAR sensor(s) 1660, coupled to a dedicated processor,DSP, FPGA, and/or ASIC, that is electrically coupled to driver feedback,such as a display, speaker, and/or vibrating component.

In at least one embodiment, RCTW system may provide visual, audible,and/or tactile notification when an object is detected outsiderear-camera range when vehicle 1600 is backing up. In at least oneembodiment, RCTW system includes AEB system to ensure that vehiclebrakes are applied to avoid a crash. In at least one embodiment, RCTWsystem may use one or more rear-facing RADAR sensor(s) 1660, coupled toa dedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

In at least one embodiment, conventional ADAS systems may be prone tofalse positive results which may be annoying and distracting to adriver, but typically are not catastrophic, because conventional ADASsystems alert driver and allow driver to decide whether a safetycondition truly exists and act accordingly. In at least one embodiment,vehicle 1600 itself decides, in case of conflicting results, whether toheed result from a primary computer or a secondary computer (e.g., firstcontroller 1636 or second controller 1636). For example, in at least oneembodiment, ADAS system 1638 may be a backup and/or secondary computerfor providing perception information to a backup computer rationalitymodule. In at least one embodiment, backup computer rationality monitormay run a redundant diverse software on hardware components to detectfaults in perception and dynamic driving tasks. In at least oneembodiment, outputs from ADAS system 1638 may be provided to asupervisory MCU. In at least one embodiment, if outputs from primarycomputer and secondary computer conflict, supervisory MCU determines howto reconcile conflict to ensure safe operation.

In at least one embodiment, primary computer may be configured toprovide supervisory MCU with a confidence score, indicating primarycomputer's confidence in chosen result. In at least one embodiment, ifconfidence score exceeds a threshold, supervisory MCU may follow primarycomputer's direction, regardless of whether secondary computer providesa conflicting or inconsistent result. In at least one embodiment, whereconfidence score does not meet threshold, and where primary andsecondary computer indicate different results (e.g., a conflict),supervisory MCU may arbitrate between computers to determine appropriateoutcome.

In at least one embodiment, supervisory MCU may be configured to run aneural network(s) that is trained and configured to determine, based atleast in part on outputs from primary computer and secondary computer,conditions under which secondary computer provides false alarms. In atleast one embodiment, neural network(s) in supervisory MCU may learnwhen secondary computer's output may be trusted, and when it cannot. Forexample, in at least one embodiment, when secondary computer is aRADAR-based FCW system, a neural network(s) in supervisory MCU may learnwhen FCW system is identifying metallic objects that are not, in fact,hazards, such as a drainage grate or manhole cover that triggers analarm. In at least one embodiment, when secondary computer is acamera-based LDW system, a neural network in supervisory MCU may learnto override LDW when bicyclists or pedestrians are present and a lanedeparture is, in fact, safest maneuver. In at least one embodiment,supervisory MCU may include at least one of a DLA or GPU suitable forrunning neural network(s) with associated memory. In at least oneembodiment, supervisory MCU may comprise and/or be included as acomponent of SoC(s) 1604.

In at least one embodiment, ADAS system 1638 may include a secondarycomputer that performs ADAS functionality using traditional rules ofcomputer vision. In at least one embodiment, secondary computer may useclassic computer vision rules (if-then), and presence of a neuralnetwork(s) in supervisory MCU may improve reliability, safety andperformance. For example, in at least one embodiment, diverseimplementation and intentional non-identity makes overall system morefault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, in at least oneembodiment, if there is a software bug or error in software running onprimary computer, and non-identical software code running on secondarycomputer provides same overall result, then supervisory MCU may havegreater confidence that overall result is correct, and bug in softwareor hardware on primary computer is not causing material error.

In at least one embodiment, output of ADAS system 1638 may be fed intoprimary computer's perception block and/or primary computer's dynamicdriving task block. For example, in at least one embodiment, if ADASsystem 1638 indicates a forward crash warning due to an objectimmediately ahead, perception block may use this information whenidentifying objects. In at least one embodiment, secondary computer mayhave its own neural network which is trained and thus reduces risk offalse positives, as described herein.

In at least one embodiment, vehicle 1600 may further includeinfotainment SoC 1630 (e.g., an in-vehicle infotainment system (IVI)).Although illustrated and described as a SoC, infotainment system 1630,in at least one embodiment, may not be a SoC, and may include, withoutlimitation, two or more discrete components. In at least one embodiment,infotainment SoC 1630 may include, without limitation, a combination ofhardware and software that may be used to provide audio (e.g., music, apersonal digital assistant, navigational instructions, news, radio,etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g.,hands-free calling), network connectivity (e.g., LTE, WiFi, etc.),and/or information services (e.g., navigation systems, rear-parkingassistance, a radio data system, vehicle related information such asfuel level, total distance covered, brake fuel level, oil level, dooropen/close, air filter information, etc.) to vehicle 1600. For example,infotainment SoC 1630 could include radios, disk players, navigationsystems, video players, USB and Bluetooth connectivity, carputers,in-car entertainment, WiFi, steering wheel audio controls, hands freevoice control, a heads-up display (“HUD”), HMI display 1634, atelematics device, a control panel (e.g., for controlling and/orinteracting with various components, features, and/or systems), and/orother components. In at least one embodiment, infotainment SoC 1630 mayfurther be used to provide information (e.g., visual and/or audible) touser(s) of vehicle, such as information from ADAS system 1638,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

In at least one embodiment, infotainment SoC 1630 may include any amountand type of GPU functionality. In at least one embodiment, infotainmentSoC 1630 may communicate over bus 1602 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of vehicle 1600. In atleast one embodiment, infotainment SoC 1630 may be coupled to asupervisory MCU such that GPU of infotainment system may perform someself-driving functions in event that primary controller(s) 1636 (e.g.,primary and/or backup computers of vehicle 1600) fail. In at least oneembodiment, infotainment SoC 1630 may put vehicle 1600 into a chauffeurto safe stop mode, as described herein.

In at least one embodiment, vehicle 1600 may further include instrumentcluster 1632 (e.g., a digital dash, an electronic instrument cluster, adigital instrument panel, etc.). In at least one embodiment, instrumentcluster 1632 may include, without limitation, a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). In atleast one embodiment, instrument cluster 1632 may include, withoutlimitation, any number and combination of a set of instrumentation suchas a speedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s),supplemental restraint system (e.g., airbag) information, lightingcontrols, safety system controls, navigation information, etc. In someexamples, information may be displayed and/or shared among infotainmentSoC 1630 and instrument cluster 1632. In at least one embodiment,instrument cluster 1632 may be included as part of infotainment SoC1630, or vice versa.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are providedbelow. In at least one embodiment, inference and/or training logic 715may be used in system FIG. 16C for inferencing or predicting operationsbased, at least in part, on weight parameters calculated using neuralnetwork training operations, neural network functions and/orarchitectures, or neural network use cases described herein.

Such components can be used to generate synthetic data imitating failurecases in a network training process, which can help to improveperformance of the network while limiting the amount of synthetic datato avoid overfitting.

FIG. 16D is a diagram of a system 1676 for communication betweencloud-based server(s) and autonomous vehicle 1600 of FIG. 16A, accordingto at least one embodiment. In at least one embodiment, system 1676 mayinclude, without limitation, server(s) 1678, network(s) 1690, and anynumber and type of vehicles, including vehicle 1600. In at least oneembodiment, server(s) 1678 may include, without limitation, a pluralityof GPUs 1684(A)-1684(H) (collectively referred to herein as GPUs 1684),PCIe switches 1682(A)-1682(D) (collectively referred to herein as PCIeswitches 1682), and/or CPUs 1680(A)-1680(B) (collectively referred toherein as CPUs 1680). GPUs 1684, CPUs 1680, and PCIe switches 1682 maybe interconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 1688 developed by NVIDIA and/orPCIe connections 1686. In at least one embodiment, GPUs 1684 areconnected via an NVLink and/or NVSwitch SoC and GPUs 1684 and PCIeswitches 1682 are connected via PCIe interconnects. In at least oneembodiment, although eight GPUs 1684, two CPUs 1680, and four PCIeswitches 1682 are illustrated, this is not intended to be limiting. Inat least one embodiment, each of server(s) 1678 may include, withoutlimitation, any number of GPUs 1684, CPUs 1680, and/or PCIe switches1682, in any combination. For example, in at least one embodiment,server(s) 1678 could each include eight, sixteen, thirty-two, and/ormore GPUs 1684.

In at least one embodiment, server(s) 1678 may receive, over network(s)1690 and from vehicles, image data representative of images showingunexpected or changed road conditions, such as recently commencedroad-work. In at least one embodiment, server(s) 1678 may transmit, overnetwork(s) 1690 and to vehicles, neural networks 1692, updated neuralnetworks 1692, and/or map information 1694, including, withoutlimitation, information regarding traffic and road conditions. In atleast one embodiment, updates to map information 1694 may include,without limitation, updates for HD map 1622, such as informationregarding construction sites, potholes, detours, flooding, and/or otherobstructions. In at least one embodiment, neural networks 1692, updatedneural networks 1692, and/or map information 1694 may have resulted fromnew training and/or experiences represented in data received from anynumber of vehicles in environment, and/or based at least in part ontraining performed at a data center (e.g., using server(s) 1678 and/orother servers).

In at least one embodiment, server(s) 1678 may be used to train machinelearning models (e.g., neural networks) based at least in part ontraining data. In at least one embodiment, training data may begenerated by vehicles, and/or may be generated in a simulation (e.g.,using a game engine). In at least one embodiment, any amount of trainingdata is tagged (e.g., where associated neural network benefits fromsupervised learning) and/or undergoes other pre-processing. In at leastone embodiment, any amount of training data is not tagged and/orpre-processed (e.g., where associated neural network does not requiresupervised learning). In at least one embodiment, once machine learningmodels are trained, machine learning models may be used by vehicles(e.g., transmitted to vehicles over network(s) 1690, and/or machinelearning models may be used by server(s) 1678 to remotely monitorvehicles.

In at least one embodiment, server(s) 1678 may receive data fromvehicles and apply data to up-to-date real-time neural networks forreal-time intelligent inferencing. In at least one embodiment, server(s)1678 may include deep-learning supercomputers and/or dedicated AIcomputers powered by GPU(s) 1684, such as a DGX and DGX Station machinesdeveloped by NVIDIA. However, in at least one embodiment, server(s) 1678may include deep learning infrastructure that use CPU-powered datacenters.

In at least one embodiment, deep-learning infrastructure of server(s)1678 may be capable of fast, real-time inferencing, and may use thatcapability to evaluate and verify health of processors, software, and/orassociated hardware in vehicle 1600. For example, in at least oneembodiment, deep-learning infrastructure may receive periodic updatesfrom vehicle 1600, such as a sequence of images and/or objects thatvehicle 1600 has located in that sequence of images (e.g., via computervision and/or other machine learning object classification techniques).In at least one embodiment, deep-learning infrastructure may run its ownneural network to identify objects and compare them with objectsidentified by vehicle 1600 and, if results do not match anddeep-learning infrastructure concludes that AI in vehicle 1600 ismalfunctioning, then server(s) 1678 may transmit a signal to vehicle1600 instructing a fail-safe computer of vehicle 1600 to assume control,notify passengers, and complete a safe parking maneuver.

In at least one embodiment, server(s) 1678 may include GPU(s) 1684 andone or more programmable inference accelerators (e.g., NVIDIA's TensorRT3). In at least one embodiment, combination of GPU-powered servers andinference acceleration may make real-time responsiveness possible. In atleast one embodiment, such as where performance is less critical,servers powered by CPUs, FPGAs, and other processors may be used forinferencing. In at least one embodiment, inference and/or training logic715 are used to perform one or more embodiments. Details regardinginference and/or training logic 715 are provided elsewhere herein.

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. Term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to, orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinrange, unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. Use of term “set” (e.g., “a set of items”) or “subset,” unlessotherwise noted or contradicted by context, is to be construed as anonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, term “subset” of acorresponding set does not necessarily denote a proper subset ofcorresponding set, but subset and corresponding set may be equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. For instance, in illustrative example of a set havingthree members, conjunctive phrases “at least one of A, B, and C” and “atleast one of A, B and C” refer to any of following sets: {A}, {B}, {C},{A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language isnot generally intended to imply that certain embodiments require atleast one of A, at least one of B, and at least one of C each to bepresent. In addition, unless otherwise noted or contradicted by context,term “plurality” indicates a state of being plural (e.g., “a pluralityof items” indicates multiple items). A plurality is at least two items,but can be more when so indicated either explicitly or by context.Further, unless stated otherwise or otherwise clear from context, phrase“based on” means “based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium, for example, in form of acomputer program comprising a plurality of instructions executable byone or more processors. In at least one embodiment, a computer-readablestorage medium is a non-transitory computer-readable storage medium thatexcludes transitory signals (e.g., a propagating transient electric orelectromagnetic transmission) but includes non-transitory data storagecircuitry (e.g., buffers, cache, and queues) within transceivers oftransitory signals. In at least one embodiment, code (e.g., executablecode or source code) is stored on a set of one or more non-transitorycomputer-readable storage media having stored thereon executableinstructions (or other memory to store executable instructions) that,when executed (i.e., as a result of being executed) by one or moreprocessors of a computer system, cause computer system to performoperations described herein. A set of non-transitory computer-readablestorage media, in at least one embodiment, comprises multiplenon-transitory computer-readable storage media and one or more ofindividual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by differentprocessors—for example, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofdisclosure and does not pose a limitation on scope of disclosure unlessotherwise claimed. No language in specification should be construed asindicating any non-claimed element as essential to practice ofdisclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in particularexamples, “connected” or “coupled” may be used to indicate that two ormore elements are in direct or indirect physical or electrical contactwith each other. “Coupled” may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting examples,“processor” may be a CPU or a GPU. A “computing platform” may compriseone or more processors. As used herein, “software” processes mayinclude, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.Terms “system” and “method” are used herein interchangeably insofar assystem may embody one or more methods and methods may be considered asystem.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. Obtaining, acquiring,receiving, or inputting analog and digital data can be accomplished in avariety of ways such as by receiving data as a parameter of a functioncall or a call to an application programming interface. In someimplementations, process of obtaining, acquiring, receiving, orinputting analog or digital data can be accomplished by transferringdata via a serial or parallel interface. In another implementation,process of obtaining, acquiring, receiving, or inputting analog ordigital data can be accomplished by transferring data via a computernetwork from providing entity to acquiring entity. References may alsobe made to providing, outputting, transmitting, sending, or presentinganalog or digital data. In various examples, process of providing,outputting, transmitting, sending, or presenting analog or digital datacan be accomplished by transferring data as an input or output parameterof a function call, a parameter of an application programming interfaceor interprocess communication mechanism.

Although discussion above sets forth example implementations ofdescribed techniques, other architectures may be used to implementdescribed functionality, and are intended to be within scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, variousfunctions and responsibilities might be distributed and divided indifferent ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A method comprising: identifying a first set ofimages comprising a plurality of objects of a plurality of classes;providing the first set of images as input to a first machine learningmodel trained to detect, for a given input image, a presence of one ormore objects of at least one of the plurality of classes depicted in thegiven input image and to predict at least mask data associated with oneor more of the detected objects; determining, from one or more firstoutputs of the first machine learning model, object data associated witheach of the first set of images, wherein the object data for eachrespective image of the first set of images comprises mask dataassociated with each object detected in the respective image; andtraining a second machine learning model to detect objects of a targetclass in a second set of images, wherein the second machine learningmodel is trained using at least a subset of the first set of images, anda target output for the at least a subset of the first set of images,wherein the target output comprises the mask data associated with eachobject detected in the at least a subset of the first set of images andan indication of whether a class associated with each object detected inthe at least a subset of the first set of images corresponds to thetarget class.
 2. The method of claim 1, wherein the first machinelearning model is further trained to predict, for each of the one ormore detected objects, a particular class of the plurality of classesassociated with a respective detected object.
 3. The method of claim 2,further comprising: generating the target output, wherein generating thetarget output comprises: determining whether the particular classassociated with the respective detected object corresponds to the targetclass.
 4. The method of claim 1, further comprising: identifying, usingan indication of one or more bounding boxes associated with the image,ground truth data associated with the respective object depicted in theimage.
 5. The method of claim 4, at least one bounding box of the one ormore bounding boxes were provided by at least one of an acceptedbounding box authority entity or a user of a platform.
 6. The method ofclaim 1, wherein the second machine learning model is a multi-headmachine learning model, and wherein the method further comprises: upontraining the second machine learning model using the at least a subsetof the first set of images and the target output, identifying one ormore heads of the second machine learning model that correspond topredicting mask data for a given input image; and updating the secondmachine learning model to remove the one or more identified heads. 7.The method of claim 6, further comprising: providing a third set ofimages as input to the second machine learning model; obtaining one ormore second outputs of the second machine learning model; anddetermining, based on the one or more second outputs, additional objectdata associated with each of the third set of images, wherein theadditional object data for each respective image of the second set ofimages comprises an indication of a region of the respective image thatincludes an object detected in the respective image and a classassociated with the detected object.
 8. The method of claim 6, furthercomprising: transmitting the updated second machine learning model to atleast one of an edge device or an endpoint device via a network.
 9. Asystem comprising: a memory device; and a processing device coupled tothe memory device, wherein the processing device is to performoperations comprising: generating training data for a machine learningmodel, wherein generating the training data comprises: generating atraining input comprising an image depicting an object; and generating atarget output for the training input, wherein the target outputcomprises a bounding box associated with the depicted object, mask dataassociated with the depicted object, and an indication of a classassociated with the depicted object; providing the training data totrain the machine learning model on (i) a set of training inputscomprising the generated training input and (ii) a set of target outputscomprising the generated target output; identifying one or more heads ofthe trained machine learning model that correspond to predicting maskdata for a given input image; and updating the trained machine learningmodel to remove the one or more identified heads.
 10. The system ofclaim 9, wherein the operations further comprise: providing a set ofimages as input to the updated trained machine learning model; obtainingone or more outputs of the updated trained machine learning model; anddetermining, from the one or more outputs, object data associated witheach of the set of images, wherein the object data for each respectiveimage of the second set of images comprises an indication of a region ofthe respective image that includes an object detected in the respectiveimage and a class associated with the detected object.
 11. The system ofclaim 9, wherein the operations further comprise: deploying the updatedtrained machine learning model using at least one of an edge device oran endpoint device.
 12. The system of claim 9, wherein generating thetarget output for the training input comprises: providing the imagedepicting the object as input to an additional machine learning model,wherein the additional machine learning model is trained to detect, fora given input image, a presence of one or more objects depicted in thegiven input image and to predict at least mask data associated with oneor more of the detected objects; and determining, from one or moreoutputs of the additional machine learning model, object data associatedwith the image, wherein the object data for the image comprises maskdata associated with the depicted object.
 13. The system of claim 12,wherein the additional machine learning model is further trained topredict, for each of the one or more detected objects, a classassociated with the respective detected object, and wherein object datafor the image further comprises the indication of the class associatedwith the depicted object.
 14. The system of claim 9, wherein generatingthe target output for the training input comprises: obtaining groundtruth data associated with the image, wherein the ground truth datacomprises the bounding box associated with the depicted object.
 15. Thesystem of claim 14, wherein the ground truth data is obtained from adatabase comprising an indication of one or more bounding boxesassociated with objects depicted in a set of images, wherein the imageis included in the set of images, and wherein the one or more boundingboxes is provided by an accepted bounding box authority entity or a userof a platform.
 16. A non-transitory computer readable storage mediumcomprising instructions that, when executed by a processing device,cause the processing device to perform operations comprising: providinga set of current images as input to a first machine learning model,wherein the first machine learning model is trained to detect objects ofa target class in a given set of images using (i) a training inputcomprising a set of training images, and (ii) a target output for thetraining input, the target output comprising, for each respectivetraining image of the set of training images, ground truth dataassociated with each object depicted in the respective training image,wherein the ground truth data indicates a region of the respectivetraining image that includes a respective object, mask data associatedwith each object depicted in the respective training image, wherein themask data is obtained based on one or more outputs of a second machinelearning model, and an indication of whether a class associated witheach object depicted in the respective training image corresponds to thetarget class; obtaining one or more outputs of the first machinelearning model; and determining, based on the one or more outputs of thefirst machine learning model, object data associated with each of theset of current images, wherein the object data for each respectivecurrent image of the set of current images comprises an indication of aregion of the respective current image that includes an object detectedin the respective current image and an indication of whether thedetected object corresponds to the target class.
 17. The non-transitorycomputer readable storage medium of claim 16, wherein the object datafurther comprises mask data associated with the object detected in therespective current image.
 18. The non-transitory computer readablestorage medium of claim 16, wherein determining object data associatedwith each of the set of current images comprises: extracting one or moresets of object data from the one or more outputs of the first machinelearning model, wherein each of the one or more sets of object data isassociated with a level of confidence that the object data correspondsto an object detected in the respective current image; and determiningwhether the level of confidence associated with a respective set ofobject data satisfies a level of confidence criterion.
 19. Thenon-transitory computer readable storage medium of claim 16, furthercomprising training the first machine learning model by: providing theset of training images as input to the second machine learning model,wherein the second machine learning model is trained to detect, for agiven input image, one or more objects of at least one of a plurality ofclasses depicted in the given input image and to predict, for each ofthe one or more detected objects, at least mask data associated with therespective detected object; determine, from one or more outputs of thesecond machine learning model, object data associated with each of theset of training images, wherein the object data for each respectivetraining image of the set of training images comprises mask dataassociated with each object detected in the respective image.
 20. Thenon-transitory computer readable storage medium of claim 16, wherein theground truth data is obtained using a database comprising an indicationof one or more bounding boxes associated with the set of trainingimages, wherein each of the one or more bounding boxes were provided byat least one of an accepted bounding box authority entity or a user of aplatform.